Site Selection: a Case Study in the Identification of Optimal Cysteine Engineered Antibody Drug Conjugates

Affiliations.

  • 1 Binghamton University, School of Pharmacy and Pharmaceutical Sciences, P.O. Box 6000, Binghamton, New York, 13902-6000, USA. [email protected].
  • 2 Biomedicine Design, Pfizer, Inc., Cambridge, Massachusetts, 06379, USA.
  • 3 Worldwide Research and Development, Pfizer, Inc., 445 Eastern Point Road, Groton, Connecticut, 06379, USA.
  • 4 PKDM, Amgen, Inc., 360 Binney Street, AMA 1, Cambridge, Massachusetts, 02142, USA.
  • 5 Oncology Research and Development, Pfizer, Inc., 401 N. Middletown Rd., Pearl River, New York, 10965, USA.
  • 6 AbbVie, Inc., 100 Research Dr, Worcester, Massachusetts, 01605, USA.
  • 7 International Flavors and Fragrances, 521 West 57th Street, New York, New York, 10019, USA.
  • 8 Maverick Therapeutics, Inc, 3260 Bayshore Blvd, Brisbane, California, 94005, USA.
  • PMID: 28439809
  • DOI: 10.1208/s12248-017-0083-7

As the antibody drug conjugate (ADC) community continues to shift towards site-specific conjugation technology, there is a growing need to understand how the site of conjugation impacts the biophysical and biological properties of an ADC. In order to address this need, we prepared a carefully selected series of engineered cysteine ADCs and proceeded to systematically evaluate their potency, stability, and PK exposure. The site of conjugation did not have a significant influence on the thermal stability and in vitro cytotoxicity of the ADCs. However, we demonstrate that the rate of cathepsin-mediated linker cleavage is heavily dependent upon site and is closely correlated with ADC hydrophobicity, thus confirming other recent reports of this phenomenon. Interestingly, conjugates with high rates of cathepsin-mediated linker cleavage did not exhibit decreased plasma stability. In fact, the major source of plasma instability was shown to be retro-Michael mediated deconjugation. This process is known to be impeded by succinimide hydrolysis, and thus, we undertook a series of mutational experiments demonstrating that basic residues located nearby the site of conjugation can be a significant driver of succinimide ring opening. Finally, we show that total antibody PK exposure in rat was loosely correlated with ADC hydrophobicity. It is our hope that these observations will help the ADC community to build "design rules" that will enable more efficient prosecution of next-generation ADC discovery programs.

Keywords: PK exposure; antibody drug conjugate; hydrophobicity; linker stability; plasma stability.

  • Amino Acid Sequence
  • Cysteine / chemistry*
  • Immunoconjugates / chemistry*
  • Molecular Dynamics Simulation
  • Immunoconjugates

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Case Study Site Selection: Using an Evidence-Based Approach in Health-Care Settings

  • By: Gail V. Barrington , Rena Shimoni & Augusto V. C. Legaspi
  • Product: Sage Research Methods Cases Part 1
  • Publisher: SAGE Publications, Ltd.
  • Publication year: 2014
  • Online pub date: January 01, 2014
  • Discipline: Anthropology , Education , Nursing , Political Science and International Relations , Sociology
  • DOI: https:// doi. org/10.4135/978144627305013510255
  • Keywords: acute care , long-term care , scope , scope of practice , site selection , surveying Show all Show less
  • Online ISBN: 9781473946965 Copyright: Contact SAGE Publications at http://www.sagepub.com More information Less information

This case study illustrates distinctive features of case study methodology that were responsive to context in ways not often seen in case study research. Located in Alberta, Canada, the study explored the factors that supported or hindered licensed practical nurses' ability to work to the full scope of practice assigned to them by legislation but that they had not been able to fully implement in the field. While the case study method as described by Robert K. Yin provides a number of useful design components, there are several limitations, most particularly with regard to lack of rigor and the potential for bias. Due to the politically charged environment of this study, particular attention was given to devising an objective method for the selection of case study sites as well as to a number of other strategies to strengthen study rigor. An extensive literature review, theory development, a research framework, the success case method, a province-wide survey, and statistical modeling were used to produce an objective and defensible platform for site selection as well as to enhance study rigor. Study findings were well received by the diverse stakeholders who represented key sectors in the health-care system.

Learning Outcomes

  • Gain an understanding of the research components required in case study design
  • Understand the limitations of case study research and learn some strategies to mitigate these weaknesses
  • Learn how evidence can be strengthened by the use of multiple data sources
  • Understand the value of conducting a literature review to highlight methodological challenges observed in other research projects so that they can be avoided in future
  • Understand how theory building can provide a strong foundation for case study research
  • Learn how a research framework can provide a useful structure for tool design, data analysis and synthesis, and the preparation of individual and cross-case reports

This case study describes the recent study conducted to explore the impact of workplace factors on a particular group of health-care professionals. It tells the story of how, as researchers, we changed our methods in response to a complex environment and stakeholders' need for an evidence-based approach.

Coming into the study as Research Director, I was convinced that my experience in case study methodology would make the whole thing pretty straightforward. Between 1992 and 2008, I had conducted five program evaluation studies using case study methodology. I had explored a wide range of topics from customer service training in tourist facilities to new ways of teaching science in schools. I had written 34 individual case study reports, and because I like to write, I enjoyed doing this very much.

I based my work on a close reading of the work of case study methodologist, Robert K. Yin. I used a revised version of his 1984 classic reference book, Case Study Research: Design and Methods (SAGE, 1989). While a number of revised editions have been published since, I preferred my dog-eared copy, complete with highlighting, underlining, and marginal notes.

These early case study projects were certainly challenging in terms of the time, effort, and resources required, but I do not recall questioning the methodology itself. The case study method seemed self-contained and robust, and I replicated it in each study. Now, however, those experiences seem simplistic and lacking in rigor. The complex context and political sensitivities evident in this study caused me to rethink my assumptions about case study research. A much different approach would be the result.

The Research Context

In 2006, in the western Canadian province of Alberta, legislation was changed to allow licensed practical nurses (LPNs) to use a broader set of skills in health-care settings. LPN training was expanded from a 1-year certificate to a 2-year diploma, and programs were offered at a number of community colleges. Working to full scope became the expectation for LPNs, but change in practice was slow. By 2009, their annual professional survey results suggested that only 50% of LPNs were working to full scope. So what was the problem?

This study was commissioned in 2011 to find out more information about LPNs' scope utilization by the Workforce Division of the provincial government's department of health. Resource constraint and workforce efficiency were key drivers, but staff also needed sound evidence on which to base decisions related to policy and staffing. As a result, the study became a priority.

The grant flowed through the College of Licensed Practical Nurses of Alberta (CLPNA) to Bow Valley College in Calgary, Alberta. The college had a large LPN training program and a department that specialized in applied research and evaluation. I was hired as Research Director and acted, with my colleague, Dr Rena Shimoni, as Co-Principal Investigators. A longtime LPN practitioner and former instructor became our Project Manager. A steering committee was established to provide guidance. It included a number of carefully selected stakeholders as well as some nurse researchers to ensure that all groups with a vested interest in study outcomes were represented.

We wanted to find out what workplace factors were influencing the LPNs' ability to use their new skills. Typically, they worked in care teams with registered nurses (RNs) and health-care aides (HCAs). Each of the three nursing groups had their own training and certification processes and their own skill sets. Each group was paid on a different wage scale. While there were some clearly delineated tasks assigned to each group, in other areas, there was role overlap. The LPNs generally found themselves to be the ones ‘in the middle’.

The relationship between the RN and LPN professional organizations was strained. There was much anecdotal evidence of turf battles. Some RNs feared that LPNs would take over part of their jobs. At the same time, many LPNs felt undervalued by RNs, many of whom are unaware of the new legislation and upgraded training.

In reality, the range of education for both groups was quite broad. While RNs continued to upgrade their own skills, some of their most senior members had graduated when a 2- or 3-year diploma program had been the norm. Similarly, some senior LPNs had graduated many years before from a 6-month certificate program. The confusion and misinformation generated by these issues were evident in both the literature and public discourse. As researchers, we could see that this topic was clearly complex, political, and very, very tricky.

The Case Study Method

We proposed using a comparative case study design to conduct six case studies in different health-care facilities around the province. We looked to Yin for the guidance we needed. As he pointed out, case study research was useful to understand complex social phenomena within a real-life context. It provided a good way to explore interventions with no clear single set of boundaries or outcomes. These rationales certainly described our research context, but Yin also pointed out some methodological limitations:

  • the time-consuming nature of the research
  • the massive documentation that is produced
  • the limited control an investigator has over actual events
  • the lack of rigor and the potential for bias
  • the inability to generalize study findings

He confessed that case studies were typically viewed as a ‘less desirable form of inquiry’ than either experiments or surveys. As a result, he suggested using strategies to mitigate these limitations such as referring to the literature, using multiple sources of evidence, and adhering closely to the research design.

There were five components that Yin believed were especially important:

  • The study's question
  • The study propositions
  • Its units of analysis
  • The logic linking the data to the propositions
  • The criteria for interpreting the findings

Some of these components, particularly the units of analysis, left a lot of discretion to the researchers. Keeping in mind the critical mind-set of our stakeholders, some of whom would be looking for any hint of bias in the study, we needed to develop strategies that would enhance objectivity, increase rigor, and produce defensible evidence. And so we embarked on a journey to modify the case study method to fit the complex demands of our research environment.

Gathering the Evidence

We took the advice of Rossi, Lipsey, and Freeman (2004), and within the loose structure of the case study method, chose to be as rigorous as possible. We wanted to establish a confident basis for action, to withstand any criticism that might try to discredit our study, and to ensure that our information would be judged sufficient under scientific standards.

The Literature Review

To get us started and to inform our approach, we conducted an extensive literature review. It had three objectives:

  • To understand available evidence in order to provide a strong foundation for the study
  • To highlight the methodological challenges associated with examining one professional group working within a complex, interactive team
  • To identify gaps in knowledge associated with the impact of LPNs' scope utilization on quality of care

Very quickly we discovered that little research had focused specifically on LPNs or on other equivalent occupations (e.g. ‘registered practical nurse’.). Out of over 150,000 publications with the word ‘nurse’ cited in the PubMed database, only 374 included the term ‘licensed practical nurse’, and only 29 referred to scope of practice. We extended our search to unpublished policy documents and reports, and eventually, we identified nearly 100 documents for review. We produced a report that summarized our findings (Shimoni et al., 2011) and circulated this early product to stakeholders.

Many of the studies on scope that we reviewed had methodological problems, data limitations, or attribution issues. Flaws included unreported variables, confounding factors, small sample sizes, inappropriate use of summarized scores and aggregated data, and attribution issues associating outcomes to specific team members. Still, it was clear that some of these studies continued to influence nursing thought.

Building a Theory

Our next step was to build a theory to test our assumptions. Our study purpose as stated by our funder read as follows:

To provide objective, research-based evidence focused on LPNs in typical health-care settings and to explore the factors that promote and/or inhibit successful LPN scope utilization.

Initially, we had proposed a set of research questions, but these were refined after we had reviewed the literature. Several nurse researchers on our steering committee also offered us some sound advice. The final questions were as follows:

  • What can we learn about LPNs' individual practice that promotes or inhibits their ability to practice to full scope? How can these supports be enhanced? How can these barriers be reduced?
  • What can we learn about LPNs' work teams and systems that promote or inhibit their ability to practice to full scope? How can these supports be enhanced? How can these barriers be reduced?
  • What can we learn about LPNs' organizations that promote or inhibit their ability to practice to full scope? How can these supports be enhanced? How can these barriers be reduced?
  • Is there any evidence of differences in the patient experience when LPNs are working to their full scope? What are these differences?

We theorized that four key factors influenced LPNs' scope of practice in the workplace. These included (1) the individual LPN and related characteristics, (2) the care team in which the LPN worked, (3) the organization or site in which the LPN worked, and (4) the patient or client and their required nursing care. We designed the Scope of Practice Factors Model, and it provided the theory that guided our study (see Figure 1 ).

Figure 1. Scope of Practice Factors Model.

None

The Research Framework

We developed a research framework or Data Collection Matrix (DCM) to link our model to the research questions. Many of the topics identified in the literature were linked to the four key factors and provided the basis for study sub-questions and related indicators. We used the DCM to guide our tool development, and all tools were coded to its numbering system. Later that same numbering system was used to code and track the data we collected. This created a structured evidence trail that lead directly from the model through the DCM to tool development, data collection, data analysis, data synthesis, and final report preparation. The excerpt from the DCM in Figure 2 shows the links between research questions, indicators, tools, and item numbers. By checking back and forth between the model, the DCM, and the tools, we continually sharpened the study focus.

Figure 2. Data Collection Matrix.

None

The Success Case Method

In the past, when identifying case study sites, I had developed a simple sampling framework (e.g. rural vs urban and large vs small) and filled in the cells with reasonable or accessible choices. Now faced with heightened demands for rigor, that approach felt like throwing darts to see where they landed. Everyone we talked to had a recommendation for a ‘good’ case study site. We questioned the wisdom of this approach because it was based on personal opinion.

Needing a stronger rationale, we turned to the success case method developed by Robert O. Brinkerhoff (2003) to understand the impact of training. He claimed that it was a fast, credible, and effective way to evaluate organizational change. He believed that we learn the most about a phenomenon by interviewing both those individuals who are the most successful at implementing change and those who are the least. The separation of high and low scope sites seemed a promising way to understand scope utilization issues.

However, there was a small problem. Brinkerhoff's method was predicated on having survey data. He suggested setting high and low cutoff scores on several survey items and then randomly selecting individuals from each group (i.e. most successful and least successful). Thus, in order to identify high and low scope sites in an unbiased way, we first needed to survey all LPNs in the province.

Luckily, the steering committee could see the value of our suggested selection method and approved the addition of a province-wide survey. There were a number of reasons why this was a good idea, particularly because the survey allowed us to go beyond perceptions of scope to explore actual recorded practice. We used the competencies identified by the CLPNA as the basis for assessing actual scope. We also asked questions about site location and work setting and, based on the literature, added questions asked about the work environment, including communications, team work, safety culture, job satisfaction, and stress.

We sent out 8549 surveys to all practicing LPNs providing both an online and mail-in option; 2313 LPNs responded for a response rate of 27%. While we would have liked a higher return rate, we found that the respondents tracked proportionately to staff deployment across the province. The absolute number of returns also added to our confidence.

In the end, the decision to add a survey to our design strengthened our study immeasurably. The mix of quantitative and qualitative methods added depth and credibility to our findings, but it also allowed us explore a number of issues more fully. By staging the research over two phases, we had time to refine our focus as we went, so that by the time we actually visited the sites, we knew a lot about more about LPN characteristics and salient workplace issues than we would have if we had gone there directly as initially planned. We were able to refine our case study tools based on survey findings, focusing quickly on key topics. For us, administering a survey first followed by in-depth case studies was a winning strategy.

Cluster Analysis

We hired our colleagues at Science-Metrix, an evaluation firm located in Montréal, Québec, to conduct our statistical analysis and to help us with site selection. We asked them to provide four categories of sites with three possible selections in each one. The categories included

  • acute care sites in which LPNs work to low scope,
  • acute care sites in which LPNs work to high scope,
  • long-term care sites in which LPNs work to low scope,
  • long-term care sites in which LPNs work to high scope.

For their analysis, the research analysts, David Campbell and Olivier Beauchesne, selected Question 28 (Q28) of the survey (see Figure 3 ). They determined that a score of 1 would be associated with a low scope of practice and 5 with a high scope. They produced aggregated statistics at the site level and removed respondents with less than 75% valid answers (9 out of 12 items). Invalid answers were considered to be blanks or ‘not applicable’ answers. They also removed sites where less than five respondents had replied. In the end, 52 sites remained in the analysis.

Figure 3. Q28 competencies used.

None

Three statistical procedures were performed on these data:

  • a factorial analysis distinguished between Q28 variables that occurred more often in either acute care or long-term care settings. Dimension 1 variables related mainly to the administration of intravenous medications or blood products, more common in acute care. Dimension 2 variables related to developing and revising care plans and to teaching clients and families, more common in long-term care. The two dimensions were plotted on a graph.
  • based on their average score for each of the 12 items in Q28, sites were clustered into four groups, discriminated by their propensity to allow LPNs to practice to full scope. This procedure was called k-clustering (see Figure 4 ).
  • multi-criteria analysis was used to find the sites that performed highest or lowest in either acute care or long-term care settings. Scope performance was displayed on the graph by making the size of the bubble proportional to the site score.

Figure 4. Cluster graph with the final six sites chosen for case studies.

None

Based on this analysis, the research analysts drew up a list of recommended sites and forwarded it to the research team.

Selecting the Case Study Sites

The research team reviewed the list of suggested sites. Only at this final stage did qualitative considerations enter our deliberations so our choices lay within the parameters of the list produced through statistical analysis. As a final screen, we added some inclusion and exclusion criteria. For example, we wanted sites that were not too technically dependent on a specific treatment or too specialized in their target population (see Table 1 ).

Table 1. Inclusion and exclusion criteria.

None

Finally, we considered geographic location and size. We developed a table of high and low scope sites and identified our first and second choices in each cell. Invitations were sent to all the first choice sites. We were really excited when all six agreed to participate.

The final sample included three acute care sites, one mixed site (providing both acute and long-term care), and two long-term care sites. Three sites were high scope and three were low; three were urban and three were rural. Now we could proceed with our research knowing that our sites had been selected based on the best possible evidence. No dartboards for us!

What Happened Afterward

Of course, the site selection activity occurred early in the research process. To provide multiple lines of evidence, we used a number of tools, adapting standardized instruments where possible. We used standardized tools to measure patient and family experience. Where no tool was available, we created our own tools based on the DCM. These included four interview guides (for senior administrators, team leaders, RNs, and LPNs) and a focus group protocol for the HCAs. These tools were validated extensively but even so, once we were in the field, we still modified some of the wording after our first site visit.

The data were collected by a team of two researchers, including me and a junior researcher. Logistical support was provided by our experienced LPN Project Manager. We recorded each interview digitally, taking notes as a backup. Although we had many minor adventures in the field, because we had planned our study so carefully, the research rolled out smoothly, and ultimately, we collected data from 193 individuals across the six sites.

The recorded data were transcribed into individual Word documents, validated by a second researcher, and imported into MAXQDA, a qualitative software program. Data summaries were compiled and organized by DCM topic and emergent theme. Finally, the information was summarized in narrative form in six case study reports, using the Scope of Practice Factors Model and the DCM to organize our material.

The reports were sent to the site administrators who each reviewed their own report for accuracy. They sent us corrections as needed and also completed a validation survey rating the report's validity, relevance, utility, and value. All were satisfied that the reports reflected LPN scope issues and suggested that the information would be useful. The following comments (from both high and low scope sites) were typical:

I found the report to be an impartial and balanced perspective of the LPN scope of practice. I appreciated that it included possible areas for further study and some direction on care/assignment aspects that may require some education and discussion with all staff. Thank you for involving us in this study. (Senior Administrator, Site 2)

It was a privilege to be involved in this study. I found the report fascinating. Although I thought I understood the LPN role at this site well, it was very advantageous to see the promoters and barriers that relate to work setting and scope of practice summarized in a table. This summary has prompted me to think about other ways we could utilize our work force at this site … This report is excellent and is an excellent tool. Thank you for the data. (Senior Administrator, Site 4)

Each site report was revised based on administrator feedback and was returned to them for their use. A high level of confidentiality was employed throughout the process. The administrators were never told if their site was characterized as high or low scope, and no one else ever saw these reports.

As part of the final report, we prepared a cross-case summary, using our model as an organizing guide. Factors that promoted or inhibited scope utilization were described. No site names were mentioned, and only very general setting descriptors were used, such as urban, rural, acute, and long-term care.

To ensure rigor, we created a data triangulation table to summarize the key findings across all the sources of evidence including the literature review, survey, case studies, and key informant interviews. Thus, we were able to demonstrate which case study findings concurred with or strengthened previous studies, which findings were new, and which, if any, contradicted the reported literature.

The final report provided a great deal of well-documented evidence about LPN scope of practice. The results and recommendations were well received by the steering committee and other diverse players in the health-care system. The report was approved for circulation, and the CLPNA posted the complete report on their website. The research team was invited to present key findings to the annual LPN conference, and a panel of key stakeholders responded with comments about their own next steps.

This case study research explored the factors that supported or hindered LPNs' ability to work to the full scope of practice assigned to them by legislation but not yet fully implemented in the field. While the case study method as described by Yin provides a number of useful design components, there are several limitations, most particularly with regard to lack of rigor and the potential for bias. This study devised an objective method for the selection of case study sites and also used a number of strategies to strengthen study rigor throughout. An extensive literature review, theory building, a research framework, the success case method, a province-wide survey, and sophisticated statistical modeling were used to produce an objective and defensible platform for site selection.

Six case study sites were identified representing high or low scope work places for LPNs, environments in which they were either supported or hindered in their ability to use the competencies for which they were trained. The use of a survey greatly enhanced the information obtained by the study, and the two-phased approach allowed the researchers to incorporate early findings into later research activities.

The resulting case studies provided a rich and detailed description of six particular health-care sites in Alberta and as such have been able to inform the broader discussion about LPNs' scope of practice. While decision makers have suggested that they will use the evidence produced in this study for policy change, it is too soon to tell what impact it will eventually have.

Exercises and Discussion Questions

  • If you were to choose the sites but you did not have the resources to conduct such an extensive survey, what other methods can you think of in order to identify the high and low scope utilization sites? What are the reliability and validity strengths and issues of these options?
  • Could additional research be conducted to see if the key findings from this study could be generalized beyond the findings of these six sites? What would you do next?
  • In this research, a quantitative survey was used to identify sites with high and with low utilization of scope for the case studies. Can you think of a situation when a quantitative survey may follow completion of the case studies?
  • Do you think that the view still exists that case studies are considered a less desirable form of inquiry? On what do you base this opinion? What changes have occurred (e.g. in society, in people's mind-sets, and in research) that may have influenced present thinking about case study methodology?
  • This study took place in a complex, politically charged health system. Can you give an example of another system where the traditional case study method would be insufficient? Are there further steps beyond the ones described here that would be necessary to ensure rigor in this type of context?
  • In your own area of research, what features of case study methodology would be the most important? What rationale would you develop to support using this methodology in this context?

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  • Open access
  • Published: 11 December 2019

Criteria for site selection in industry-sponsored clinical trials: a survey among decision-makers in biopharmaceutical companies and clinical research organizations

  • Tilde Dombernowsky   ORCID: orcid.org/0000-0001-8930-1341 1 ,
  • Merete Haedersdal 1 ,
  • Ulrik Lassen 2 &
  • Simon Francis Thomsen 1 , 3  

Trials volume  20 , Article number:  708 ( 2019 ) Cite this article

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Metrics details

Knowledge of what the pharmaceutical industry emphasizes when assessing trial sites during site selection is sparse. A better understanding of this issue can improve the collaboration on clinical trials and increase knowledge of how to attract and retain industry-sponsored trials. Accordingly, we investigated which site-related qualities multinational biopharmaceutical companies and clinical research organizations (CROs) find most important during site selection.

An online survey among decision-makers for trial site selection in the Nordic countries employed at multinational biopharmaceutical companies and CROs was conducted. The respondents’ experiences with and perceptions of site selection were addressed to evaluate the relative importance of site-related qualities. We included up to four respondents per company, representing different geographic regions. Descriptive statistics were used to summarize findings.

Of 49 eligible companies, 20 biopharmaceutical companies and 23 CROs participated. In total, 83 responses were analyzed (estimated response rate 78%). A relative importance of site-related qualities was identified: For example, 88% (binomial 95% confidence interval [CI] ±7%) preferred reaching enrollment goals at trial sites in their region 10% quicker rather than cutting the costs at all sites by 20%. Likewise, 42% (CI ±11%) of the respondents preferred that trial sites were best at having the first patients ready for inclusion right after site initiation visit compared to having good data entry, documentation, and reporting practice (25% [CI ±9%]), easily reachable site personnel and backup (23% [CI ±9%]), fast contractual procedure times (6% [CI ±5%]), a key opinion leader associated with the site (3% [CI ±4%]), and updated equipment and facilities (1% [CI ±2%]). In total, 75% [CI ±9%] agreed that their company would be interested in cooperating with an inexperienced trial site if the site had access to a large patient population and 52% [CI ±11%] had experienced that their company selected an inexperienced trial site in favor of an experienced site due to a higher level of interest and commitment.

Conclusions

This study indicates that recruitment-related factors are pivotal to the pharmaceutical industry when assessing trial sites during site selection. Data quality-related factors seem highly valued especially in early phase trials whereas costs and investigator’s publication track record are less important. Experience in conducting clinical trials is not imperative. However, this applies primarily to late phase trials.

Peer Review reports

When the pharmaceutical industry assesses potential trial sites during trial site selection, multiple aspects are considered. Factors such as patient population availability, resources at the site, and data collection procedures are evaluated. Likewise, site personnel-related qualities such as interest and commitment, communicative skills, and experience in conducting clinical trials are taken into account. Today, site management is often handled by clinical research organizations (CROs) as many clinical trials are outsourced [ 1 ]. Consequently, CROs play a pivotal role during site selection alongside the affiliates of biopharmaceutical companies.

Knowledge of what the pharmaceutical industry emphasizes when selecting European trial sites is sparse; to our knowledge, only two publicly available studies have investigated this [ 2 , 3 ]. They indicate that recruitment-related factors are pivotal whereas costs are less important. Moreover, they suggest that experience in conducting clinical trials is not imperative.

A better understanding of what the pharmaceutical industry emphasizes when assessing trial sites during site selection can improve the collaboration and performance in clinical trials, ultimately leading to improved medical care. Moreover, a better understanding of this issue can extend knowledge of how trial sites can attract and retain industry-sponsored trials. Accordingly, we conducted a survey among decision-makers for trial site selection in biopharmaceutical companies and CROs to further explore this area.

The aim of this study was to investigate which site-related qualities multinational biopharmaceutical companies and CROs find most important during site selection and while running clinical trials in the Nordic countries. In continuation of the findings by Gehring et al. [ 3 ] and findings we made in an interview study conducted in 2016 [ 2 ], we particularly focused on recruitment-related factors, costs, and experience in conducting clinical trials. Three main assumptions generated from this previous research were explored:

Biopharmaceutical companies and CROs find that recruitment-related factors (i.e. patient population availability, timely patient recruitment, and startup time) are the most important factors during site selection and while running clinical trials;

Experience in conducting clinical trials is not imperative to biopharmaceutical companies and CROs when selecting clinical trial sites;

The costs of running a clinical trial are secondary to biopharmaceutical companies and CROs if trial sites recruit the patients agreed upon in a timely matter.

Identification of companies and respondents

Our recruitment strategy focused on personal contacts to ensure that relevant companies and respondents were included. First, we identified companies involved in trial site selection in one or more Nordic countries. Thereafter, we identified suitable respondents within each company. Figure  1 illustrates the company selection process.

figure 1

Flow chart showing the identification of eligible companies * The Danish Association of the Pharmaceutical Industry, The Swedish Association of the Pharmaceutical Industry, The association for the pharmaceutical industry in Norway, Pharma Industry Finland, The trade association and forum for clinical research organizations active in Sweden, The CRO network of Trial Nation Denmark. # CRO clinical research organization

The following inclusion criteria for the companies were set:

Multinational biopharmaceutical company or CRO;

Conducted clinical trials in one or more Nordic countries;

The affiliate(s) / local office(s) of the company were involved in trial site selection in one or more Nordic countries;

Member of one of the following organizations: The Danish Association of the Pharmaceutical Industry; The Swedish Association of the Pharmaceutical Industry; the association for the pharmaceutical industry in Norway; Pharma Industry Finland; the trade association and forum for clinical research organizations active in Sweden; and the CRO network of Trial Nation Denmark.

The following inclusion criteria for the respondents were set:

Employed at one of the included companies at a Nordic affiliate / local office;

Decision-maker for trial site selection in one or more Nordic countries or involved in the recommendation of trial sites to the sponsor(s).

Using the trial registry ClinicalTrials.gov [ 4 ], we estimated that the member companies of the included organizations sponsor or are collaborators in 79% of all industry-sponsored clinical trials conducted in the Nordic countries (Additional file 1 ). Consequently, we believe that we included the majority of companies involved in trial site selection in the Nordic countries.

Eligible companies and respondents were identified through contact with the Nordic and European affiliate(s) or office(s) by email or phone. A contact person—who in most cases was also a respondent—was sent a link to the online survey and forwarded the link to other eligible participants within the company. We included up to four respondents per company, representing different geographic regions (Denmark, Finland, Norway, and Sweden) as decision-makers for trial site selection employed at the same company may have different perceptions on site selection depending on the region in which they operate. Respondents were recruited continuously during the whole survey response period from 8 May to 8 October 2018. The period was expanded for 1.5 months due to the summer holidays. Because of the recruitment design, the identity of most respondents was known to the authors. However, the respondents were assured that the results would be published without any disclosure of their identity. No remuneration was provided but a summary of the survey results before publication was offered. Additional information on the recruitment process and survey distribution is displayed in the Additional file 1 .

Content of the survey

The survey was a web-based questionnaire addressing the respondents’ perceptions of factors that influence trial site selection in the Nordic countries. Some items aimed at the respondents’ personal opinions, whereas others aimed at the overall opinion of their company. The survey consisted of a background information section followed by three main sections and was completed in 10–15 min using the SurveyXact online platform [ 5 ]. The items were presented primarily in Likert scale, single response, and ranking format. In the first section, respondents were asked to indicate their level of agreement with different statements using a five-point scale (strongly agree, agree, undecided, disagree, strongly disagree). In the second section, the respondents’ own experiences with site selection at their company were addressed using primarily single response questions; in the last section, ranking questions were used to evaluate which site-related qualities are the most important in different situations. To avoid missing data, all questions had to be answered before continuing to the next section. To minimize response bias, response categories of the ranking questions were randomly ordered for each respondent individually.

Due to differences in the organizational structure and function of the companies, some items had to be differently formulated depending on the respondent being employed at a biopharmaceutical company or a CRO. Therefore, the two respondent groups received a different questionnaire, although the content was almost identical. For example, during pretesting, CRO respondents stressed that CROs are recommending trial sites to the sponsor and not selecting trial sites. Therefore, the word selected was replaced with recommended in relevant items as illustrated in Table  1 . We believe that the different wording of the items ensured a homogeneous interpretation of each item across the two respondent groups, still making it possible to evaluate the items as one. However, two items were evaluated separately as the wording differed markedly (Table 1 , question 5 and 6; Fig.  2 , questions 2 and 3). The full survey for biopharmaceutical and CRO respondents, respectively, are displayed in Additional file 1 .

figure 2

Levels of agreement with statements about trial site selection in the Nordic countries # This question applied to only biopharmaceutical respondents ( n  = 43) ¤ This question applied to only CRO respondents ( n  = 40). CRO clinical research organization

Two items in the background section served to ensure that the respondent and the company were indeed decision-makers for trial site selection. If this was not confirmed, the respondent was excluded. Further, the respondent’s company email address was requested to verify that the response came from a relevant person, to determine which company was involved, and to avoid duplicate responses.

Development and validation of the survey

The development of the survey was based on a previous interview study including employees involved in trial allocation at multinational biopharmaceutical companies [ 2 ] and other literature within this field [ 3 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. First, we developed an exhaustive list of site-related qualities that the pharmaceutical industry potentially considers during site selection. Subsequently, the items of the survey were constructed, repeatedly reviewing the list, and the three main assumptions that we aimed to investigate. The design and content of the survey were discussed among the authors and iteratively with relevant clinical trial stakeholders and two statisticians. The initial items were scrutinized to mitigate ambiguity and identify concepts that needed to be validated during pretesting, such as early phase clinical trial and data quality . These concepts were listed and systematically reviewed during pretesting. The pretesting included 19 potential respondents employed at different companies and was carried out at meetings lasting 45–75 min, using a standardized procedure. Additional information on the development and validation of the survey is displayed in Additional file 1 .

Statistical analysis and sample size considerations

We used descriptive statistics to summarize findings. Binomial 95% confidence intervals (CIs) were calculated using the equation for the normal approximation for the binomial confidence interval: p ± z 1-α/2 √(p (1-p)/n). To evaluate potential differences in responses across the two respondent groups, we compared responses using Chi-squared tests and Fisher’s exact tests. Ranking questions were evaluated by comparing differences in the number of first rankings within each response category across the two respondent groups. As the number of respondents in each group was small, we also considered the true values observed. Data were analyzed using SPSS Version 25. A p value threshold of ≤ 0.05 was considered statistically significant. There were no missing data as all responses were complete. Given the descriptive design and a finite number of respondents, we did not formally estimate a required sample size.

Of the 49 eligible companies, 20 biopharmaceutical companies (83%) and 23 CROs (92%) participated in the survey (Fig.  1 ). The number of decision-makers for trial site selection in the Nordic countries varied between the companies that differed markedly in size and organizational structure. A total of 101 responses were received, of which none were duplicate. Six were partial and all excluded as they were < 20% completed. Further, two were excluded as the respondents reported not to be decision-makers for trial site selection. We received more than one response per Nordic country from four companies. Consequently, 10 responses from these companies were excluded randomly using SPSS. In total, 83 responses were analyzed: 43 from biopharmaceutical companies and 40 from CROs. The average number of respondents per company was 1.9 (standard deviation [SD] 1.1), and the estimated response rate was 78% for both respondent groups (see Additional file 1 ). The respondents’ type of position and level of experience are displayed in Table  2 .

Recruitment-related factors (assumption 1)

In total, 84% (CI ±8%) of the respondents strongly agreed or agreed that recruitment-related factors are the site-related qualities that their company values the most (Fig. 2 , question 9). Likewise, 88% (CI ±7%) preferred reaching enrollment goals at trials sites in their region 10% quicker rather than cutting the costs at all sites by 20% (data not shown). When asked to rank which information about a trial site unknown to their company that the company would find the most valuable, recruitment and retention track record was ranked first by 71% (CI ±10%) of the respondents among the six factors tested (Additional file 1 : Figure S1). Similarly, when the respondents were asked what they would prefer that trial sites were best at, 42% (CI ±11%) ranked having the first patients ready for inclusion right after site initiation visit first (Fig.  3 ).

figure 3

What decision-makers for trial site selection would prefer that Nordic trial sites were best at* * Respondents ( n  = 83) were asked: If you could choose, what would you prefer that trial sites were best at? The six response categories were ranked from one to six, one being the most important. MR mean ranking (of the response category), SD standard deviation

Figure  4 illustrates the ranking of five site-related qualities according to importance during site selection. For early phase trials, having a large patient population available at the site was ranked first by 33% (CI ±10%), whereas it was 54% (CI ±11%) for phase III trials. Two items addressed which of three site-related qualities the clinical operations departments at the affiliates value the most while running an early phase and phase III trial, respectively. Timely patient recruitment was ranked the highest in both cases (57% [CI ±11%] and 59% [CI ±11%], respectively) compared to timely data entry and reporting (10% [CI ±6%] and 12% [CI ±7%], respectively) and no critical or major findings at the site during the trial (33% [CI ±10%] and 29% [CI ±10%], respectively) (Additional file 1 : Figure S2). As illustrated by Fig.  5 , overestimation of the available study population and insufficient site personnel resources or backup at the site are the site-related qualities that most often cause delay in patient recruitment at Nordic trial sites according to the respondents.

figure 4

Relative importance of site-related qualities for early phase ( a ) and phase III trials ( b )* * Respondents ( n  = 83) were asked which of five site-related qualities their company finds the most important during site selection for an early phase clinical trial and phase III clinical trial, respectively. The five response categories were ranked from one to five, one being the most important. MR mean ranking (of the response category), SD standard deviation

figure 5

Site-related factors that do most often cause delay in patient recruitment at Nordic trial sites* * Respondents ( n  = 83) were asked to choose among 12 site-related factors the four factors they believe most often cause delay in patient recruitment at the Nordic trial sites that their company cooperates with. Only factors that trial sites influence were included

Two items addressed which factors the headquarters of biopharmaceutical companies find the most important when evaluating the affiliates’ performance and CROs’ performance, respectively, regarding running clinical trials. For both early phase and phase III trials, timely patient recruitment was ranked first by most respondents (58% [CI ±11%] and 57% [CI ±11%], respectively) compared to high data quality (35% [CI ±10%] and 24% [CI ±9%], respectively), timely data entry and reporting (4% [CI ±4%] and 10% [CI ±6%], respectively), and low costs of running the clinical trial (3% [CI ±4%] and 9% [CI ±6%], respectively) (Additional file 1 : Figure S3).

Experience in conducting clinical trials (assumption 2)

In total, 75% (CI ±9%) strongly agreed or agreed that their company would be interested in cooperating with an inexperienced trial site if the trial site had access to a large patient population (Fig. 2 , question 6). Further, 52% (CI ±11%) had experienced that their company selected an inexperienced trial site in favor of an experienced site due to a higher level of interest and commitment (Table 1 , question 1). In contrast, 74% (CI ±9%) of the respondents strongly agreed or agreed that it is un likely that their company would include an inexperienced trial site for an early phase trial; for phase III trials, it was only 25% (CI ±9%) (Fig. 2 , questions 4 and 5).

Respondents were asked to rank which of three site personnel-related qualities their company finds the most important during site selection: Experience in conducting clinical trials was ranked first by 59% (CI ±11%) for early phase trials and 46% (CI ±11%) for phase III trials, whereas impression of a high level of interest and commitment was ranked first by 33% (CI ±10%) and 48% (CI ±11%), respectively (Fig.  6 ). Most respondents believed that if trial site personnel seek out stakeholders at biopharmaceutical companies at conferences displaying a site profile form and track record, the companies would consider including the trial site in future clinical trials: yes definitely (24% [CI ±9%]); yes maybe (70% [CI ±10%]); and no (6% [CI ±5%]).

figure 6

Relative importance of site personnel-related qualities for early phase ( a ) and phase III trials ( b )* * Respondents ( n  = 83) were asked which of three site personnel-related qualities their company finds the most important during site selection for an early phase clinical trial and phase III clinical trial, respectively. The three response categories were ranked from one to three, one being the most important. MR mean ranking (of the response category), SD standard deviation

Costs (assumption 3)

Most respondents strongly agreed or agreed that the costs of running a clinical trial at a trial site are secondary if the site recruits the patients agreed upon in a timely matter (Fig. 2 , questions 2 and 3). Likewise, when asked which site information is the most valuable to their company, prices of all trial-related services was ranked the lowest alongside data on potential investigators’ publication track record and job position (Additional file 1 : Figure S1). Similarly, low costs at the site was ranked lowest among five site-related qualities regarding their importance during site selection (Fig.  4 ). For both early phase and phase III trials, low costs of running the clinical trial was ranked lowest when considering which factors the headquarters find the most important when evaluating the affiliates and CROs (ranked fourth by 70% [CI ±10%] and 63% [CI ±10%], respectively) (Additional file 1 : Figure S3).

Sensitivity analysis

Overall, the response pattern was similar across the two respondent groups. However, more biopharmaceutical than CRO respondents preferred trial sites having the first patients ready for inclusion right after site initiation visit (ranked first by 56% [CI ±15%] and 28% [CI ±14%], respectively ( p  = 0.014)) rather than sites having good data entry, documentation, and reporting practice (19% [CI ±12%] and 33% [CI ±15%], respectively ( p  = 0.207)). Moreover, notable differences occurred regarding which factors the headquarters of biopharmaceutical companies value the most when evaluating the affiliates’ and CROs’ performance in relation to running clinical trials. Timely patient recruitment was ranked first by more biopharmaceutical than CRO respondents for both early phase and phase III clinical trials (65% [CI ±14%] vs 50% [CI ±15%] for early phase trials [ p  = 0.187]; and 74% [CI ±13%] vs 38% [CI ±15%] for phase III trials [ p  = 0.001]). Conversely, low costs of running the clinical trial was ranked first by more CRO respondents (8% [CI ±8%] vs 0% of biopharmaceutical respondents for early phase trials [ p  = 0.108]; and 18% [CI ±12%] vs 2% [CI ±5%] for phase III trials [ p  = 0.026]).

In this survey that investigated which site-related qualities the pharmaceutical industry values the most during site selection in the Nordic countries, recruitment-related factors were strongly emphasized, whereas costs and investigator’s publication track record generally had low priority. Data quality-related factors and experience in conducting clinical trials were strongly emphasized in early phase trials, whereas experience was less emphasized in phase III trials.

Recruitment-related factors were highly emphasized throughout the survey for both early phase and phase III clinical trials. This gives weight to the supposition that access to the relevant patient population, a fast startup time, and timely recruitment are among the most important factors when the pharmaceutical industry evaluates trial sites during site selection. Nevertheless, the survey results also indicate that other qualities are sometimes more important. For example, we found that 61% of the respondents had experienced that their company selected a trial site which delivered an insufficient recruitment in prior trials, because a key opinion leader was associated with the site (Table 1 , question 3).

According to the respondents, one of the main reasons for insufficient recruitment at Nordic trial sites is overestimation of the available study population at the site, when considering factors that trial sites influence. This concurs with findings in our previous interview study in which the participants reported that they often find the investigators’ recruitment projections over-optimistic [ 2 ]. Consequently, their company routinely marks down these. This has also been reported by others [ 13 , 14 ]. Trial sites should take this into consideration and strive to make accurate recruitment projections by carefully considering aspects of the current trial rather than following “gut intuition” or replicating estimations from prior similar trials. That said, the sponsors are also responsible for inaccurate recruitment projections. First, the investigators typically do not have full protocol information when requested to estimate the number of participants the trial site can recruit and the information given by the sponsor changes over time. Second, the response deadline is short, limiting time for a thorough assessment. Third, trial sites are not economically compensated for the time spent which impedes investigators’ motivation to make thorough estimations. As recruitment projections strongly influence study timelines, accurate projections should be of high priority among both trial sites and sponsors to mitigate trial extensions and failure.

Data quality-related factors were generally emphasized less than recruitment-related factors in this survey. One explanation could be that sufficient patient recruitment is crucial to the success of a trial whereas good data quality is not. Another explanation could be that the companies have only little influence on recruitment whereas they can more easily ensure sufficient data quality by allocating extra resources to monitoring and training at the site. However, the results do not confirm this assumption, as responses were ambiguous in this matter (Fig. 2 , questions 8 and 10). In our previous interview study, only half of the participants spontaneously mentioned data quality-related factors as important [ 2 ]. Moreover, like in this survey, some believed that the headquarters of their company did not value data quality as high as timely patient recruitment. However, when asked, the participants stressed that they find high data quality indispensable. Possibly, these findings reflect that high data quality is essential; however, as there are no data without participants, recruitment is emphasized more than data quality during site selection.

Interestingly, the survey results suggest that biopharmaceutical companies and CROs are interested in collaborating with inexperienced trial sites if they have access to the relevant patient population and show interest and commitment. Moreover, interest and commitment is supposedly as important as experience in conducting clinical trials during selection for phase III trials. This concurs with findings by Gering et al. [ 3 ] who asked 341 different clinical trial stakeholders to divide 100 points across five investigator-related qualities when selecting trial sites for a phase III/IV trial ( investigator recruitment/retention track record , experience in previous trials , interest , concurrent workload , and publication track record ). They found that interest was rated as high as experience in previous trials (mean 22.4 [SD 13.4] and 22.7 [SD 12.0], respectively). In accordance with our study, investigator’s publication track record was least important. The Danish Association of the Pharmaceutical Industry (LIF DK) has also found that commitment is important during site selection. In 2015, LIF DK asked their member companies to describe which site-related qualities they emphasize for early phase trials (personal correspondence with LIF DK). It was stressed that site personnel’s expertise, dedication, and availability are particularly important. Additionally, it was mentioned that the member companies often cooperate with the same preferred trial sites in early phase trials which makes it challenging for inexperienced trial sites to gain cooperation on early phase trials. This is in line with the results of our survey and previous interview study [ 2 ] that propose that experience in conducting clinical trials is more important during selection for early phase that late phase trials. This is unsurprising, as early phase trials are usually operationally complex and demand a high level of expertise.

Our results clearly indicate that costs are less important than other factors during site selection, which concurs with previous findings [ 2 , 3 , 8 ]. Nonetheless, this does not necessarily mean that costs are unimportant; costs may play an essential role during country selection, thereby indirectly influencing site selection. Interestingly, our results suggest that costs are of higher influence when the headquarters evaluate the performance of CROs than the performance of their own affiliates. Given the fact that CROs are external partners, this is unsurprising.

We believe that trial sites that already meet the site personnel and facilities requirements necessary to be considered for selection may benefit from emphasizing three aspects in particular during site selection: (1) a thorough and sound assessment of the patient population available at the site; (2) a high level of interest and commitment among site personnel; and (3) a good data entry, documentation, and reporting practice. Further, trial sites that wish to attract industry-sponsored clinical trials will possibly benefit from seeking out stakeholders from the pharmaceutical industry displaying a site profile form and track record. Trial sites should keep in mind that the recruitment performance at one trial site influences the allocation of trials to all sites in the region as the headquarters of biopharmaceutical companies may not allocate future trials to a region delivering an insufficient patient recruitment.

Strengths and limitations

We believe that this study displays interesting and credible findings. The internal validity of the study is high as the survey was thoroughly constructed and pretested; the respondents were individuals with good reading comprehension who use similar terminology. However, the study has limitations. The number of respondents included in this survey was low; fewer companies than expected were involved in trial site selection in the Nordic countries and several companies had only one primary decision-maker for all Nordic countries. Nevertheless, the respondents were highly representative of the population that we wanted to investigate and the response rate was high. Moreover, the survey included most companies involved in trial site selection in the Nordic countries. To ensure sufficient survey completion, we had to strictly limit the completion time. Consequently, relevant items were omitted which limits the interpretation of the results. Additionally, some site-related qualities were not evaluated. For example, a company’s prior experience with a site is important when selecting trial sites [ 13 , 15 ]. However, the importance of a good working relationship with the site or site personnel having the right mindset is difficult to evaluate in a quantitative setting. We suspected that all qualities would be rated as highly important if they were simply rated individually. Instead we used ranking questions to assess the relative importance of the site-related qualities. However, this method may lead to more “satisficing” behaviour as rank ordering potential responses is a higher level cognitive task.

The present study indicates that recruitment-related factors are pivotal to the pharmaceutical industry when assessing trial sites during site selection. Data quality-related factors seem highly valued especially in early phase trials, whereas costs and investigator’s publication track record are generally less important. Experience in conducting clinical trials is not imperative; biopharmaceutical companies and CROs are supposedly interested in cooperating with inexperienced trial sites if they have access to the relevant patient population. However, this applies primarily to late phase trials.

This is one of the first studies investigating which qualities at a trial site the pharmaceutical industry values the most when deciding which trial sites to preferably cooperate with. Hopefully, the findings will contribute to improved collaboration and performance in industry-sponsored clinical trials and help trial sites gain involvement in these trials. In future studies, it would be highly relevant to explore the investigators’ and trial sites’ perspective. For example, little is known about what motivates investigators and trial sites to conduct clinical trials, and what they emphasize when cooperating with the pharmaceutical industry. This area should be further investigated as it is key to understanding how countries and trial sites can attract and retain industry-sponsored clinical trials as well as how to better the cooperation and performance in clinical trials.

Availability of data and materials

The full survey for biopharmaceutical and CRO respondents, respectively, are displayed in Additional file 1 . The dataset analyzed during the current study are not publicly available to ensure anonymity of the survey participants but are available from the corresponding author on reasonable request.

Abbreviations

  • Clinical research organizations

The Danish Association of the Pharmaceutical Industry

Standard deviation

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Acknowledgements

The authors thank The Capital Region of Denmark and the Research Committee at Bispebjerg- and Frederiksberg hospital who funded this study.

This study was funded by a research grant from The Capital Region of Denmark and the Research Committee at Bispebjerg- and Frederiksberg hospital. The funders were not involved in the research, preparation, or writing of this article, nor in the decision to submit it for publication.

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Tilde Dombernowsky, Merete Haedersdal & Simon Francis Thomsen

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Contributions

All authors were involved in the study design and contributed to the intellectual content of the manuscript. TD conducted the pretesting meetings and collected the data. Data analysis was conducted by TD and SFT. TD developed the first draft of the manuscript. MH, UL, and SFT contributed to the critical revision of the results and revised the manuscript. The final version of the manuscript is approved by all authors. TD is the gaurantor.

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Correspondence to Tilde Dombernowsky .

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Ethics approval and consent to participate.

By Danish law, this study did not require ethics approval, approval from the Danish Data Protection Agency, or other regulatory approval. Informed consent was obtained from all participants. It was stressed that the results of the survey would be published without any disclosure of the respondents who chose to participate.

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Competing interests

MH and SF declare relevant financial activities outside the submitted work: MH has received research grants from Leo Pharma, Lutronic, Novoxel, Procter and Gamble, and Sebacia, and declares loan of equipment from Cynosure Hologic, Lutronic, Novoxel, and Perfaction Technologies. SF has received research grants from AbbVie, Leo Pharma, Novartis, Sanofi, and UCB. SF has been a paid speaker for AbbVie, Eli Lilly, GSK, Leo Pharma, Novartis, Pierre Fabre, and Sanofi, and has served on advisory boards for Abbvie, Celgene, Eli Lilly, Janssen, Leo Pharma, Novartis, and Sanofi. UL has served on advisory boards for Bayer and Pfizer. TD declares to have no conflicts of interest.

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Supplementary information

Additional file 1: figure s1..

Information about trial sites that biopharmaceutical companies and CROs would find most valuable if available* * Respondents ( n  = 83) were asked: Which information about a trial site that your company has not been cooperating with before would your company find the most valuable if available? The six response categories were ranked from one to six, one being the most valuable. CRO clinical research organizations, MR mean ranking (of the response category), SD standard deviation. Figure S2. Relative importance of site-related qualities while running early phase (A) and phase III trials (B)* * Respondents ( n  = 83) were asked which of three site-related qualities the clinical operations departments at the affiliates of their company find the most important while running an early phase and phase III clinical trial, respectively. The three response categories were ranked from one to three, one being the most important. MR mean ranking (of the response category), SD standard deviation. Figure S3. The assessment of biopharmaceutical affiliates and CROs in early phase (A) and phase III trials (B)* * The biopharmaceutical-respondents ( n  = 43) were asked which of four factors the headquarters of their company find the most important when evaluating the affiliates’ performance regarding running clinical trials. For CRO respondents ( n  = 40), the question referred to the headquarters evaluation of the CRO. The four response categories were ranked from one to four, one being the most important. CRO clinical research organization, MR mean ranking (of the response category), SD standard deviation

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Dombernowsky, T., Haedersdal, M., Lassen, U. et al. Criteria for site selection in industry-sponsored clinical trials: a survey among decision-makers in biopharmaceutical companies and clinical research organizations. Trials 20 , 708 (2019). https://doi.org/10.1186/s13063-019-3790-9

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DOI : https://doi.org/10.1186/s13063-019-3790-9

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Case study focused on quest for potential Parkinson’s biomarker highlights opportunities and challenges in site selection.

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While a comprehensive site-selection process may require a significant investment of time and resources at the outset, the benefits may outweigh the long-term risks (e.g., study delays, additional financial resources, high attrition rates, inability to meet enrollment goals). 1 Developing a checklist such as the one used by the S4 Steering Committee can facilitate the site-selection process and lead to more open lines of communication between study sponsors and sites. Sites have an opportunity to share their interest, background, and experience, and sponsors can dig deeper into questions they may have about the ways which the protocol and operating objectives align. 1

To learn more about the S4 study, see the following peer-reviewed publications:

Chahine LM, Beach TG, Seedorff N, et al. Feasibility and Safety of Multicenter Tissue and Biofluid Sampling for a-Synuclein in Parkinson’s Disease: The Systemic Synuclein Sampling Study (S4). Journal of Parkinson’s Disease, in press. 2018

Beach TG, Serrano GE, Kremer T, et al. Systemic Synuclein Sampling Study (S4). Immunohistochemical Method and Histopathology Judging for the Systemic Synuclein Sampling Study (S4). J Neuropathol Exp Neurol. 2018 Aug 13. doi: 10.1093/jnen/nly056.

Visanji NP, Mollenhauer B, Beach TG, et al. Systemic Synuclein Sampling Study (S4). The Systemic Synuclein Sampling Study: toward a biomarker for Parkinson’s disease. Biomarkers in Medicine. 2017;11(4):359-368.

  • Beth Harper and David Zuckerman. “Critical Success Factors for Planning for Site Selection and Patient Recruitment Planning.” BioExecutive International (2006).

The MJFF Research Engagement Team includes James Gibaldi , MS, Associate Director; Tara Hastings , Senior Associate Director; Catherine M. Kopil , PhD, Director; Bernadette Siddiqi , MA, Associate Director; and Michelle Whitham , Research Partnerships Officer; all at The Michael J. Fox Foundation in New York, NY. To contact the MJFF Research Engagement Team, email: [email protected]

MJFF would like to acknowledge the following individuals for their contribution to the research presented in this case study: Lana Chahine, MD (Co-PI), Sherri Mosovsky, Brit Mollenhauer, MD, PhD (Co-PI), Danna Jennings, MD (ex oficio), John Seibyl, MD, Vanessa Arnedo, Lindsey Riley, Kuldip Dave, PhD,Tatiana Foroud, PhD, Thomas Beach, MD, Charles Adler, MD, PhD, Chris Coffey, PhD, Dixie Ecklund, RN and the Systemic Synuclein Sampling Study (S4); and Sarah Berk, MPH.

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  • Published: 08 March 2022

Application of choosing by advantages to determine the optimal site for solar power plants

  • Hui Hwang Goh 1 ,
  • Chunyu Li 1 ,
  • Dongdong Zhang 1 ,
  • Wei Dai 1 ,
  • Chee Shen Lim 2 ,
  • Tonni Agustiono Kurniawan 3 &
  • Kai Chen Goh 4  

Scientific Reports volume  12 , Article number:  4113 ( 2022 ) Cite this article

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Metrics details

  • Energy and society
  • Environmental impact
  • Solar energy

Solar energy is a critical component of the energy development strategy. The site selection for solar power plants has a significant impact on the cost of energy production. A favorable situation would result in significant cost savings and increased electricity generation efficiency. California is located in the southwest region of the United States of America and is blessed with an abundance of sunlight. In recent years, the state's economy and population have expanded quickly, resulting in an increased need for power. This study examines the south of California as a possibly well-suited site for the constructing large solar power plants to meet the local electricity needs. To begin, this article imposed some limits on the selection of three potential sites for constructing solar power plants (S1, S2, and S3). Then, a systematic approach for solar power plant site selection was presented, focusing on five major factors (economic, technological, social, geographical, and environmental). This is the first time that the choosing by advantages (CBA) method has been used to determine the optimal sites for solar power plant construction, with the possible sites ranked as S2 > S1 > S3. The results were then compared with traditional methods such as the multi-criteria decision-making method. The findings of this study suggest that the CBA method not only streamlines the solar power plant site selection process but also closely aligns with the objectives and desires of the investors.

Introduction

Historically, nonrenewable energy sources such as fossil fuels have been heavily relied upon to meet the energy requirements. However, its usage results in significant harmful gas emissions, which has a detrimental effect on the environment and the long-term growth of society 1 . In contrast, solar energy has the advantages of clean and low carbon emissions, which make it widely used in our life 2 . In recent years, solar energy is flourishing in different populated regions of the world to meet our energy needs and to preserve the environment.

Solar power generation is the most common way to use solar energy because of its ease of maintenance and low environmental impact. Solar power generation is predicted to significantly develop in the near future, particularly in industrial areas 3 . In the European Union (EU), solar energy is being used on a large scale to reduce the total carbon dioxide emissions 4 . According to the California Energy Commission report, by implementing solar power in the energy grid, California would roughly triple its existing electrical grid capacity and maintain a record rate of renewable energy capacity expansion over the next 25 years to achieve the state's economy-wide climate goals 5 . In this context, increasingly more solar power plants will be installed in the next decade.

However, increasing the number of solar power plants will be challenging. The lifespan of a solar power plant is roughly 25–30 years 6 . Thus, extending the lifespan of solar power plants and overcoming environmental hurdles posed by decommissioned plants at the end of their lifespan are popular topics of discussion. According to Domínguez, as more solar power plants are built, the amount of photovoltaic (PV) waste produced will dramatically increase 7 . Based on this, Farrell et al. reviewed and analyzed the recycling approaches of PV waste and assessed the potential energy value of waste PV modules to realize circular economy (CE) 8 , 9 . In the past few years, enormous progress has been made in the application and implementation of CE worldwide. The European Commission formulated a CE plan for the sustainable development of the EU in 2015 10 . Many policymakers and stakeholders are seeking to apply CE to various fields, with the solar power industry leading the way. Solar power plant construction is the basis of realizing solar energy CE. This enhances coherence among environment, economy, and society, which creates a sustainable business environment for investors.

To maximize the CE benefits of the solar power industry, the optimal site must be found for the construction of solar power plants, which requires a balance of economy, society, environment, and climate, and is regarded as a multi-criteria decision-making (MCDM) problem 11 . The existing literature mostly considers economic, environmental, and technological factors, but social factors, such as population density, are rarely mentioned 12 , 13 , 14 . With the rapid increase in the world population, factors related to social influence and human behavior are of great concern to decision-makers. Therefore, a comprehensive and connotative site selection model needs to be put forward to meet the site selection requirements. Herein, a new site selection model is proposed based on a comprehensive research background, considering economy, technology, society, geography, and environment.

Nevertheless, the realization of CE is affected by the investment decisions made by stakeholders considering the high costs of solar power plant construction. For investors, projects will not be selected that have low investment returns 15 , 16 . As a result, when faced with high-cost investments, stakeholders need to analyze the costs separately to make the risks of the projects transparent 17 . The investment cost of solar power plants is 4739 $/kW, while the investment cost of concentrating solar power plants is 5213–6672 $/kW in the United States of America 18 . The construction cost of the Crescent Dunes Solar Energy Project was $1 billion in 2015 19 . Therefore, due to the high construction costs, the investment cost in the solar power plant construction needs to be considered and analyzed to make these projects profitable for the investors. Existing studies treat cost factor by comparing its importance with other factors, which do not highlight the importance of cost and makes cost insensitive to the impact of site selection 20 , 21 . To fill this research gap, this paper considers cost as an independent factor in the process of solar power plant site selection to reflect the value of cost and to maximize investors’ return on investment.

In order to provide a comprehensive research background and reflect the value of cost, a new choosing by advantages (CBA) method is applied in this paper. The main contributions of this paper are as follows:

This paper created a comprehensive and methodical scheme for solar power plant site selection, which includes five basic factors and corresponding sub-factors: economy, technology, society, geography, and environment. Then, considering the high investment cost of solar power plant construction, this paper separates the cost from other factors to maximize investors' return on investment. The scheme is applied to support the site selection of solar power plants in California.

The CBA method is firstly used in the site selection for large solar power plants, and it provides a new solution for adequate decision-making.

This paper primarily aims to propose a valuable and meaningful scheme of solar power plant site selection to provide technical support for the realization of solar energy CE. The remainder of the study is divided into the following sections: “ Literature review ” section provides a brief review of the MCDM method and its application to the optimal site selection of solar power plants. “ Methodology ” section examines the criteria, parameters, and model for the solar power plant site; it also includes specifics on the CBA method. In “ Results and discussion ” section, the results are discussed, and the CBA sensitivity analysis is conducted. Finally, “ Conclusion ” section interprets the paper's conclusions.

Literature review

In this section, the existing research on the current MCDM methods and their application to the optimal site selection of solar power plants are briefly reviewed. MCDM is a well-known decision-making approach in operations research that encompasses a variety of techniques. Tirkolaee and his team have used MCDM to solve a series of decision-making problems, including supplier selection in the healthcare industry, enterprise business plan decision-making, and the optimal allocation of energy 22 , 23 , 24 . In recent years, the decision-making problems have gradually developed into complex MCDM problems, which are often accompanied by the subjectivity of decision-makers and the uncertainty of information.

Based on this, the fuzzy theory and concept have been developed to meet the decision-making requirement. Ali et al. proposed a complex interval-valued Pythagorean fuzzy set for green supplier chain management selection 25 . Sahu et al. proposed a method based on picture fuzzy set and rough set to solve the decision-making problem 26 . However, these methods cannot deal with soft multiset scenarios. To overcome this challenge, the concept of soft multiset and soft multiset topology are extended by Riaz to solve the MCDM problems 27 .

Progressively more MCDM methods have been developed by combining with the fuzzy concept and theory. Mishra et al. combined the technique for order preference by similarity ideal solution (TOPSIS) method with intuitionistic fuzzy weighted measures to solve the decision-making problem of the investment policy choice 28 . TOPSIS is an MCDM method based on the distance between positive and negative ideal solutions (PIS and NIS, respectively). Rani et al. extended fuzzy TOPSIS with the new divergence measures to select renewable energy sources 29 . At present, TOPSIS has proven to have good applicability in various fields, especially in site selection 30 .

The measurement alternatives and ranking according to compromise solution (MARCOS) method was developed by Stevic et al. based on the idea of TOPSIS 31 . Uluta et al. further extended MARCOS with correlation coefficient and standard deviation (CCSD) and indifference threshold-based attribute ratio analysis (ITARA) methods to the logistics system 32 . However, the main limitation of this method is that it is difficult to express the evaluation criteria correctly through explicit numerical values. Therefore, Brkovic et al. 33 presented an integrated full consistency method–MARCOS model, and Celik et al. 34 integrated the best–worst method (BWM), MARCOS, and interval type-2 fuzzy sets to avoid this limitation. From the perspective of application, the MARCOS method’s applicability in the field of site selection has not yet been proven.

Multi-attributive border approximation area comparison (MABAC) is an area boundary approximation method, and Pamucar et al. extended different MABAC methods to solve different decision problem 35 , 36 . Wang et al. developed an improved MABAC method based on the q-rung orthopair fuzzy set (Q-ROFS) environment. However, due to the limited practical use of Q-ROFS and MABAC, this combination method may not be appropriate for use in real-life problems 37 . Similar to the TOPSIS, MARCOS, and MABAC methods, the multi-attribute ideal–real comparative analysis (MAIRCA) and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods were combined with fuzzy concept, such as fuzzy analytic hierarchy process (FAHP)-VIKOR 38 and FAHP-MAIRCA methods 39 .

Different from TOPSIS, MARCOS, MABAC, MAIRCA, and VIKOR, preference ranking organization method for enrichment evaluation (PROMETHEE) is an outranking method. Researchers extended the PROMETHEE method to different scenarios. A fuzzy PROMETHEE method combined with trapezoidal fuzzy interval numbers has been applied to the automobile industry 40 . The PROMETHEE method with intuitionistic fuzzy soft sets has been extended to solve the decision-making problems with intuitionistic fuzzy information. The PROMETHEE method has great advantages when decisions to be made by experts are influenced by their respective areas of expertise, so it has been widely used for site selection 41 , 42 .

The main form of the current MCDM methods is in combination with fuzzy concept, weight determination methods, and ranking methods. This proves that the current MCDM methods are mature and can be effectively applied to decision-making involving a large number of fuzzy and uncertain factors and information, such as the site selection for solar power plants. TOPSIS 43 , PROMETHEE 44 , and VIKOR 45 have been proven to have good performance in the field of solar power plant site selection. However, in the application of TOPSIS, the factors of solar power plant site selection are not fully considered such as geographical disasters, population density, and visual impact 43 . In PROMETHEE 44 , payback period, population density, and policies are not taken into account 45 . Factors such as geographical disasters and policies are also not mentioned in VIKOR. In addition, the cost is not considered as a single component but compared with other factors in the TOPSIS, PROMETHEE, and VIKOR methods, which hides the true value of cost and reduces its influence on the decision-making results.

To overcome the challenges of the TOPSIS, PROMETHEE, and VIKOR methods in solar power plant site selection, this paper proposes a more comprehensive and meaningful scheme that incorporates CBA method and a solar power plant model involving economic, technological, geographical, environmental, and social factors. This scheme can also maximize the interests of investors and the CE of solar power projects based on the CBA method. The CBA method is a lean decision-making method built by Suhr in 1999 that supports sound decision-making using alternative advantage comparisons 46 . It can solve the MCDM problems and separate cost from other factors in the process of decision-making to fully ensure the real value of cost. The CBA method has been successfully applied in the architecture, engineering, and construction industry and has proven to be better than other traditional approaches 47 , 48 , 49 . The advantages of the CBA method are as follows 28 : (1) It provides a more transparent environment for the decision-makers. (2) It can be closely related to the context of the project, reducing the time for decision-makers to reach consensus. (3) Cost factors are considered separately to ensure its importance on decision-making results. Therefore, the CBA method is adopted for the optimal site selection for solar power plants in this study. Table 1 summarizes some advantages and limitations of the abovementioned approaches.

Methodology

Establish the criteria and factors.

Following a comprehensive review of the relevant literature and consultation with industry experts, this paper suggests 16 essential site selection factors. However, at some point throughout the site selection process, the characteristics of factors may have an effect on the output’s accuracy. To ideally solve this problem, the factors considered in this study can be classified as positive or negative, based on whether or not they contribute positively solar power plant production enhancement, respectively. Visual impact, solar irradiation potential, land type, geological disaster, policies, public attitude, and local development planning are considered beneficial criteria; in contrast, payback period, investment cost, rainfall, temperature, humidity, distance to roads, distance to substations, and population density are considered detrimental criteria. This treatment would advocate for simplifying the MCDM model and outlining the CBA model’s decision-making rules. The justification and explanation for the selection of each factor is discussed in greater detail below:

Visual impact The construction of solar power plants would have an effect on the daily life of animals and humans 52 . To maintain the long-term viability of the ecosystem, the visual impact of solar power plants must be considered during the design stage.

Solar irradiation potential This is clearly the key indicator determining whether solar power plants can be built at a particular site. Solar power plant’s ability to produce energy and save money is directly impacted by the amount of available solar energy. With higher amount of solar radiation being available, more electricity can be generated, making the electricity grid more efficient 53 .

Land type In some places, the land type and availability might be a critical factor in determining the site for solar power plant construction. Numerous countries have regulations regarding the types of land that can be used for solar power projects. Generally, it is preferable to employ construction land rather than agricultural land, as this would contravene the principle of sustainable growth.

Geological disaster This is a critical geographical factor in the development of solar power plants. If an area is prone to geological disasters, such as tsunamis and earthquakes, investors will encounter significant risks, and there is no value in installing solar power plants in such areas.

Policy It is critical to consider local policies for site selection. Solar energy generation is expensive due to technical constraints. When a country or municipal government reduces taxes while increasing energy prices, the investment rate increases, relieving the financial pressure on investors.

Social benefit Solar power plants are built to meet the interests of investors while also positively contributing to society. They will assist in promoting local businesses and creating jobs, thereby impacting local education and culture 54 .

Public attitude The development of large solar power plants is a massive and time-consuming endeavor. They often have detrimental effects on nearby inhabitants in terms of noise for example. It is necessary to perform extensive research to ascertain whether the local populace supports solar power plant construction.

Local development planning This serves as the foundation for the investment and commercial decision-making. If the local economy and social system have remained stagnant and saturated, the viability and hazards of investing in solar power plants must be evaluated.

Payback period This is a critical factor to examine when determining whether a project is worth investing in, and it is also a benchmark for decision-makers when determining a project’s profitability. When selecting a site for solar power plants, a project with a lengthy payback period is inappropriate and should not be prioritized.

Investment cost This is a critical factor when undertaking any project. It weighs the project’s expenses and benefits, and its appropriate consideration would lead to a cost-effective and dependable solution. The investment cost primarily encompasses the costs for land acquisition in this paper.

Rainfall Rainfall may damage solar panels and other construction equipment, reducing their lifespan. Solar power plants should be constructed with extreme caution in places prone to excessive precipitation.

Temperature Temperature can affect the longevity of solar power plants. Increased temperature can reduce the efficiency of solar energy conversion devices, resulting in decreased output 55 . When the average temperature is maintained at a steady and acceptable level, solar power plants can operate at maximum capacity.

Humidity Increased humidity results in less solar radiation, lowering the performance of solar energy conversion, increasing the cost of power generation 56 .

Distance to roads/substations The technical strategy must account for the distance between solar power plants and roads and substations. Solar power plants built near transformer substations will help reduce equipment transportation costs and enable easier construction of new infrastructure.

Population density This illustrates how metropolitan systems evolve. The population distribution and density are also critical variables in the solar power plant site selection process.

All of the abovementioned factors were determined with the assistance of experts and relevant institutions from around the world to bolster the viability of the site selection system and data dependability. Experts include local governments, government agencies, consultants, renewable energy specialists, project managers, quantity surveyors, engineers, architects, scientists, and stakeholders. Their knowledge and abilities ensure the logic and dependability of the system.

The procedure for the optimal site selection for a solar power plant

This research evaluates the economic, technological, environmental, geographical, and social factors of the study region, as well as the potential for solar power generation growth, to maximize the benefits from a solar power plant. A precise approach for the site selection of solar power plants has been developed.

Figure  1 illustrates the process of choosing a site for a solar power plant construction. The specific steps are described below:

figure 1

Solar power plant site selection framework.

Create a site selection model based on the 16 factors and suggest some constraints to help define possible site alternatives (S1, S2, and S3).

Collect and evaluate relevant data for each site alternative in accordance with the site selection method.

Determine the optimal site using the CBA model.

This approach would improve the precision and objectivity of the site selection process’s outcome. It must be noted that due to the low slope angle of the land in the study field, the slope and orientation of the land are not included in this research.

Study area and data collection

This study focused on the southern California counties of San Bernardino and Riverside (Fig.  2 ), which are mostly deserts, sparsely populated, and bountiful in solar energy. As a result, the majority of California’s solar projects are located in those two counties to supply electricity to western California’s metropolitan clusters. To begin, the factors indicated in Fig.  1 were used to select three suitable solar project sites (S1, S2, and S3). Subsequently, specifics about possible sites are provided. Prior to analyzing the site alternatives, this study’s data were collected, which are show in Table 2 . All data and statistics are derived from a variety of sources, including the National Renewable Energy Laboratory, the Weather Atlas website, and the Bureau of Land Management.

figure 2

Map of the study area (this image was created by QGIS software V3.14.16-Pi, URL link: https://www.qgis.org/en/site/ ) 57 .

Choosing by advantages method

CBA’s tabular approach is utilized for solar power plant site selection. As illustrated in Fig.  3 , the tabular CBA method comprises of six steps 58 :

Determining possible site alternatives. In this study, three possible site alternatives (S1, S2, and S3) are ultimately produced by imposing some constraints on the investigation. These are the site alternatives that are used to conduct the evaluation.

Defining criteria and factors. “ Literature review ” section discusses the criteria and factors that influence the site selection for solar power plants. It is worth emphasizing that the majority of the criteria and factors are quantitative, which makes the CBA method’s decision-making outputs objective and reliable.

Enumerating the characteristics of each site alternative. This process involves the experts and stakeholders developing choice rules for each criterion and factor, as well as summarizing the qualities of each site alternative.

Assessing advantages of each site alternative. This step requires the stakeholders to evaluate the merits of each site alternative based on the specified criteria and factors, which should be a straightforward undertaking.

Deciding the importance of each advantage. The decision-makers should prioritize each advantage. Participants used a scale ranging from 1 to 100 to assign varying degrees of importance. To begin, the “most critical advantage” should receive a score of 100. The following goal is to utilize the “most critical advantage” as a baseline against which the remaining advantages can be compared. The final stage is to determine each site alternative’s total importance of advantages (IofAs).

Choosing the best site alternative. The cost of each site alternative is calculated to obtain the cost–IofAs curve. The site alternative that gives the most value for money should be chosen by the stakeholders and decision-makers.

figure 3

Steps in the CBA method 58 .

Results and discussion

Results of choosing by advantages.

In contrast to the standard MCDM method, the CBA method places a premium on the relative advantages of the factors rather than their relative importance. To confirm the accuracy of the data and the method's viability, experts from around the world were enlisted to define the criteria and weigh the relative merits of each choice. As a consequence, 15 decision-making factors and criteria (left column of Table 2 ) were found, with the exception of investment cost. Figure  4 illustrates the score assigned by the experts to each factor's advantage. Clearly, professionals prefer solar irradiation potential, which has a maximum score of 100 and corresponds to the basic understanding of solar energy generation. Additionally, the overall score for technical and social variables is high, showing that decision-makers place a premium on the benefits of these two factors when selecting a solar power plant site.

figure 4

The score distribution of each factor’s advantage.

Table 2 demonstrates how the CBA method can be used to organize data in a way that makes selecting the ideal solar power plant site easier for experts and stakeholders. It can be seen that the relevant factors of each site alternative for a specific project are described in detail in the CBA model, which is helpful for decision-makers to reach a consensus quickly. To facilitate the analysis of the results, the IofAs values in this study were divided by 100. It can be seen that S2 has the highest total score of 6.17, while S1 and S3 scored 4.43 and 4.00, respectively. Figure  5 a shows how the CBA model makes decisions based on the cost and IofAs of each site alternative. Clearly, S2 had the second lowest cost and the highest IofAs value when compared to S1 and S3. S1 and S3 have similar IofAs values; however, S3 is substantially less expensive. In conclusion, S2 is the optimal site for solar power plant construction using the CBA method due to its higher cost performance, and the final ranking is S2 > S3 > S1. It can be seen that the impact of cost on the results is fully demonstrated by the CBA method.

figure 5

( a ) The ranking result of CBA method; ( b ) The final result of CBA method when the costs in S1 change.

Additionally, decision-makers can use the CBA method for decision-making based on their own needs in response to cost changes. Figure  5 b illustrates the decision-making outcome when the costs for S1 vary proportionately in this study. Clearly, as the cost of S1 is reduced, its cost performance improves. When the cost of S1 is lowered by approximately 20%, its cost performance index (I/C; the value of IofAs divided by the cost) is greater than that of S3. This signifies that S1 outperforms S3 in terms of the cost performance, and the findings of the CBA method will be changed to S2 > S1 > S3. As can be seen, the CBA method provides a flexible cost analysis, which gives decision-makers more choice.

Comparison study

To verify the advantages of the CBA method, this study used the TOPSIS and PROMETHEE methods for comparison. Among the distance methods mentioned in “ Literature review ” section, TOPSIS is one of the most mature methods applied to solar power plant site selection, which ensures the applicability of the method and the reliability of the results 59 . PROMETHEE is an outranking method, and its applicability to solar power plant site selection has been proven 42 . Therefore, by comparing the CBA method proposed in this paper with TOPSIS and PROMEHTEE can not only ensure the reliability and representativeness of comparison but also clearly show the changes in the results for the different methods.

To avoid the unrepresentativeness of the data, experts and stakeholders in the solar industry were asked to determine the importance of the factors used in the TOPSIS and PROMETHEE methods. The obtained data will be converted into triangular fuzzy numbers according to the rules listed in Table 3 and inputted as parameters into the FAHP model to obtain the final weight, as shown in Table 4 . Their knowledge and abilities ensure the availability and objectivity of the data. According to Table 5 , the ranking result based on closeness coefficients obtained from the standard TOPSIS method is S2 = 0.564 > S1 = 0.488 > S3 = 0.473. S1 is determined to be the most appropriate site for the solar power plant construction due to its high closeness coefficient value. Similarly, S3, with the lowest closeness coefficient value, was identified as the least preferred solution due to its proximity to PIS and to NIS. The final result of the PROMETHEE method is S2 = 0.045 > S1 = 0.029 > S3 =  − 0.073. This demonstrates that the classic MCDM method has the same decision-making performance.

Clearly, this result is not the same as that obtained using the proposed CBA method (S2 > S3 > S1). The fundamental reason for this is that the investment cost was factored into the TOPSIS and PROMETHEE model evaluations at the beginning, and as a result, S3 scored better than S1 due to its superior performance of other factors. In other words, the disadvantage of S3’s investment cost is outweighed by its other benefits. As a result, when traditional MCDM methods are used for decision-making, the investment cost is weighed against other factors. Unlike the typical MCDM model, the CBA model incorporates the predicted investment cost of each choice as an independent factor to constrain the result. That is, despite the fact that S1 performed brilliantly in this study and achieved a high score in a multitude of areas, due to the high estimated investment cost, investors and decision-makers will not select it.

In addition, the CBA method is more sensitive to cost changes than the traditional MCDM methods. To facilitate comparison, the same cost was determined for all site alternatives as a baseline to analyze the changes in the CBA, TOPSIS, and PROMETHEE results (Table 6 ). This paper presents two cases: Case 1 keeps the initial cost of the three site alternatives at the minimum cost ($2.98 million), and then scales up the cost of S1. To ensure the stability of the results, case 2 keeps the initial cost of the three site alternatives at the maximum cost ($3.96 million), and then scales up the cost of S1. Figures  6 and 7 show the results of the CBA, TOPSIS, and PROMETHEE methods for cases 1 and 2, respectively. Obviously, for the CBA method, the ranking results of the three site alternatives changed when the cost of S1 is increased by 10–15% for both cases 1 and 2.

figure 6

The final results for case 1 using the ( a ) CBA, ( b ) TOPSIS, and ( c ) PROMETHEE methods.

figure 7

The final results for case 2 using the ( a ) CBA, ( b ) TOPSIS, ( c ) PROMETHEE methods.

For the TOPSIS and PROMETHEE methods, when the results changed, the cost of S1 increases ranged from 15 to 20% and 25 to 30% respectively. Moreover, the results remain the same when the initial cost base of the site alternative increases (case 2). This indicates that CBA method makes the result more sensitive to the change in cost. The main reason is that the advantages of S1 in other aspects make up for S1's disadvantages in cost to varying degrees in the TOPSIS and PROMETHEE methods. Therefore, S1 will not be considered the worst option unless its cost increases so dramatically that the cost disadvantage outweighs the other advantages. For the CBA method, cost is an independent parameter and will not be interfered by other factors, which fully reveals the impact of the cost on the results. As a result, when evaluating projects with high cost, the results of the CBA method will enable decision-makers to fully consider the cost factor to reduce project risks.

Sensitivity analysis

To ensure the reliability of the CBA results and to reduce the influence of decision-makers' subjectivity on the results, five scenarios are designed to investigate how the results fluctuate when one factor's advantages change in this study. We altered the relative advantages of the social, environmental, economic, technological, and geographical factors. The details of the scenarios are as follows:

Scenarios A: Modify the IofAs of the factors associated with the social factors proportionately while maintaining the IofAs of the other factors.

Scenarios B: Modify the IofAs of the factors associated with the environmental factors proportionately while maintaining the IofAs of the other factors.

Scenarios C: Modify the IofAs of the factors associated with the economic factors proportionately while maintaining the IofAs of the other factors.

Scenarios D: Modify the IofAs of the factors associated with the technological factors proportionately while maintaining the IofAs of the other factors.

Scenarios E: Modify the IofAs of the factors associated with the geographical factors proportionately while maintaining the IofAs of the other factors.

Tables 7 and 8 show the IofAs and I/C values for each site alternative in the five different scenarios. The resulting rankings for the five scenarios are displayed in Fig.  8 , which illustrates that when the IofAs of the five factors were changed, the CBA results were all S2 > S1 > S3, indicating that the CBA results were stable in this study. Moreover, S1 and S3 are sensitive to social and technological factors. When the value of IofAs for social factors decreases or the value of IofAs for technological factors increases, the values of I/C for S1 and S3 get progressively closer.

figure 8

The sensitivity analysis results for scenarios A to E.

This paper begins with the discussion of CE and considers that choosing an optimal site for solar power plants is an important way to promote the CE of renewable energy. Considering the high cost for the construction of solar power plants, this paper separates the cost from the other factors in the process of solar power plant site selection to provide investors with the maximum investment return. Considering the complexity of solar power plant construction, this study proposes a scheme that incorporates the CBA method and a solar power plant model involving economic, technological, geographical, environmental, and social factors to provide technical support for optimal site selection in California. This scheme also provides a new way of thinking for investors to realize the CE of solar energy.

This study has also demonstrated that the CBA method has a good performance in the decision-making of the optimal site selection for solar power plants. In the scenarios set up in this article, the appropriate ranking for the site alternatives using the CBA method is S2 > S1 > S3. The results show that the CBA method can provide more transparent and objective decision-making than the traditional MCDM methods, making the task easier for the decision-makers. The CBA method can fully reflect the impact of cost on decision-making and allows the experts and stakeholders to make a sagacious choice based on the cost analysis to reduce the risk of the project.

The main limitation of this article is that it does not discuss the treatment of PV modules after the end of the lifespan of a solar power plant. Future research will closely link the optimal site selection of solar power plants by considering waste recycling and other relevant factors related to the CE.

Data availability

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Acknowledgements

This work was supported by the Guangxi University Junwu scholar research funding No. A3020051008.

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Hui Hwang Goh, Chunyu Li, Dongdong Zhang & Wei Dai

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Goh, H.H., Li, C., Zhang, D. et al. Application of choosing by advantages to determine the optimal site for solar power plants. Sci Rep 12 , 4113 (2022). https://doi.org/10.1038/s41598-022-08193-1

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case study of site selection

Comprehensive Review of the Landfill Site Selection Methodologies and Criteria

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This paper presents a comprehensive review of the methodological frameworks and criteria used for municipal solid waste landfill selection. The review is based on 89 scientific papers published in peer reviewed journals from 1983 onwards. The descriptive statistical analyses of the reviewed papers consider temporal, location-based quantitative, and qualitative factors. The papers considered are classified by the country where the case studies were carried out, and the qualitative ranking is performed according to the number of citations. Afterwards, the employed methods and criteria for landfill site selection were extensively analyzed and classified. The summary of the conducted analyses shows that Geographical Information Systems (GIS), either as an individual technique or in combination with other approaches are extensively used. Weighted linear combination is the most frequently applied multi-criteria decision analysis method for ranking of alternatives. The analytical hierarchy process is the dominating method for weighting the criteria. A combination of GIS with Remote Sensing techniques is used in several landfill siting studies as a more appealing approach, due to the capability of real-time data updates. The evaluations of the landfill siting criteria indicate that the most frequent main criterion is environmental, followed by economic and social criteria, while the most preferred sub-criteria is distance to the surface waters. These findings and classifications are beneficial to both, the researchers and decision makers, while serving as a support to the complex and difficult process of real-world landfill site selection.

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Donevska, K., Jovanovski, J. & Gligorova, L. Comprehensive Review of the Landfill Site Selection Methodologies and Criteria. J Indian Inst Sci 101 , 509–521 (2021). https://doi.org/10.1007/s41745-021-00228-2

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Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles (2018)

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46 To study and find the conditions amenable to dedicating lanes for CAV users, the team con- ducted a modeling and simulation-based study of CAV driver behavior on DLs on a selected set of diverse case-study sites. This chapter details the process that was used to select the case study sites based on the project objectives. The team identified a set of evaluation criteria to assess the case study sites. Figure 4.1 presents the overall approach to identifying and selecting the case study sites used for modeling CACC DLs. As shown in Figure 4.1, a set of initial candidate case study sites was created based on the team members’ extensive experience with modeling managed lanes and CACC applications. Evalua- tion criteria pertaining to case study site characteristics, managed lane characteristics, and CAV modeling feasibility were developed to down-select these case study sites to two or three that could help define guidelines for agency use in determining whether their specific applications would merit lane dedication. Case study site characteristics include features that define their operational and geographic characteristics, demand, modes, ITS strategies, and the existence of managed lanes. Managed lane characteristics include the features of the existing (or proposed) managed lane facility. These characteristics include operational rules, priority conditions, allowable modes, and access features. The team used CAV modeling feasibility to rank the test sites. 4.1 Initial List of Candidate Sites The team analyzed nine case study sites that were available for use in the modeling effort. Each candidate site represented a simulation-based corridor model for which a managed lane facility existed or had been proposed. The map in Figure 4.2 shows the initial candidate case study sites. Because of map scaling, some candidate test beds overlap (i.e., the candidate sites in St. Paul and Minneapolis, Minnesota, and in Maryland and Northern Virginia). Table 4.1 shows the preliminary list of case study sites that were evaluated and assessed for their effectiveness in achieving the project goals. The next section presents a brief description of the geographic and modeling characteristics of these candidate case study sites. 4.1.1 I-66 Corridor, Northern Virginia The candidate site on the I-66 corridor in Fairfax, Virginia, starts from the outside of the Capital Beltway (I-495) and extends for 13 miles as a 4-lane freeway segment that includes an HOV lane on the left-most lane and stretches to the west all the way through the interchange with US-29 to SR-234 (see Figure 4.3). C H A P T E R 4 Case Study Site Selection

Case Study Site Selection 47 This suburban test site includes six interchanges and two dedicated on-and-off ramps for an HOV lane that is separated from the GPLs. The average distance between interchanges is approximately 1.2 miles, yielding 0.6 miles and 2 miles of minimum and maximum interchange spacing, respec- tively. The test site experiences recurring congestion caused by high directional daily demand every weekday for the eastbound lanes (i.e., toward Washington, D.C.) during the a.m. peak and the west- bound lanes (i.e., toward Fairfax, Virginia) during the p.m. peak. Between 2:00 p.m. and 8:00 p.m., traffic volumes of the test bed range from 900 vphpl to 2,100 vphpl and include approximately Initial List of Case Study Sites Feasibility of Modeling CAV Applications Characteristics of the Managed Lane Facility Characteristics of the Case Study Sites Selected Case Study Sites Figure 4.1. Selection process for the case study sites. Source: NCHRP 20‐102(08) project team; base map from www.HERE.com. Figure 4.2. Initial candidate case study site mapping.

48 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles 1,500 vphpl of peak HOV traffic volumes. This simulation model is currently available in the U.S.DOT’s Open Source Application Development Portal for academic/research use. Based on field observations, the existing simulation model includes a traffic stream with varying vehicle compositions (FHWA Class 4 and above). The existing freeway deploys several ITS strategies along the corridor—hard shoulder running, lane use control signals, VMS, and advanced ramp metering. US-29 is a parallel arterial and an alternate route to I-66 and is acces- sible via the six interchanges included in the existing model. Currently, the parallel roadway is not included in the simulation model. The I-66 managed lanes operate as far-left, single-lane, time-of-day HOV-2 lanes in both eastbound and westbound directions. User-type restrictions along the existing HOV-2 lanes allow only vehicle classes with two or more vehicle occupancy requirements. The existing managed HOV-2 lanes operate on a time-of-day basis with restrictions applying during a.m. and p.m. peak periods on weekdays. No physical barrier separates the managed HOV-2 lanes from the mixed-use lanes. Currently, only double solid white lane markings are used to No. Case Study Corridor Location Length of Corridor Freeway Average Annual Daily Traffic (AADT) Range 1 I- 66 Northern Virginia 13 150,000–160,000 2 US 101 San Mateo, California 8.5 200,000–250,000 3 I-15 San Diego, California 22 250,000–300,000 4 I-35 MnPASS Lanes St. Paul, Minnesota 15 39,000–125,000 5 I-94 St. Paul–Minneapolis, Minnesota 14 132,000–179,000 6 I-290 Managed Lanes Chicago, Illinois 14.5 159,000–211,000 7 I-75 HOV Lanes Detroit, Michigan 18.5 105,000–180,000 8 I-270 Corridor Maryland 26 175,000–270,000 9 I-95 Express Lanes Miami, Florida 20 94,000–260,000 Table 4.1. Initial candidate case study sites. Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.3. I-66 case study site coverage.

Case Study Site Selection 49 separate the lanes and to indicate no lane changing and no access. Access points between the dedicated HOV-2 lanes and the mixed-use lanes are permitted only along areas with dashed lane striping. The I-66 also has hard shoulder running lanes on the far-most right lanes in both directions. These lanes operate from 5:30 a.m. to 11:00 a.m. in the eastbound direction and from 2:00 p.m. to 8:00 p.m. in the westbound direction. Lane utilization is indicated via VMS, which show a green arrow for permitted use and a red cross for closed for use unless exiting. The simulation case study was developed and calibrated using the PTV Vissim micro- simulation software, which allows external API-based control of simulation components, including driver behavior, making it a good candidate for CAV modeling. Driver behavior was calibrated to replicate field-observed corridor travel time, speed, and traffic volume. Freeway speed and volume information for the case study site are available, in 5-minute inter- vals and classified by lanes, via the FHWA’s Saxton Transportation Operation Laboratory (U.S.DOT 2018). The existing ITS strategies along the corridor were included in the traffic simulation model. 4.1.2 US-101 Corridor, San Mateo, California The US-101 case study site is located within the County of San Mateo, California, and stretches from Redwood City to the City of Burlingame. The length of the modeled US-101 freeway facil- ity is approximately 8.5 miles, with a parallel arterial, El Camino Real (SR-82), of similar length. Drivers can divert to the parallel arterial via seven possible interchanges. The extent and coverage of the US-101 corridor model is illustrated in Figure 4.4. Source: NCHRP 20-102(08) project team; base map from www.openstreetmap.org. Figure 4.4. US-101 case study site coverage.

50 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles 4.1.3 I-15 Corridor, San Diego, California The I-15 case study site is made up of a 22-mile stretch of the I-15 corridor facility and associ- ated parallel arterials. It extends north-to-south from the interchange with SR-78, just below the City of Escondido, California, to the interchange with Balboa Avenue, approaching San Diego, California. This facility is shown in Figure 4.5. The corridor passes through a suburban area. A network of arterials runs concurrent with the I-15 freeway, and drivers on the Interstate are able to divert via 18 possible interchanges, including Pomerado Road and Ted Williams Parkway. The I-15 corridor has a ramp metering information system and traffic light synchroniza- tion, both used for an active traffic demand management system. Speed and volume detectors are located throughout the freeway. Existing ITS strategies, specific to active traffic demand management systems, were included in the existing model. Within the limits of the simulation model, congestion during peak periods has been recorded to be approximately 50% higher than Source: NCHRP 20-102(08) project team; base map © San Diego Geographic Information Source (SanGIS) 2015, accessed at San Diego Association of Governments (SANDAG) website (www.sandag.org). Legend ICM Network Figure 4.5. I-15 case study site coverage.

Case Study Site Selection 51 off-peak hours in the peak direction. The measured daily VMT varies from the average value of all days observed by no more than a 10% margin. The simulation model includes varying heavy vehicle percentages within the traffic stream by time of day. No transit vehicles were included in the model. The I-15 freeway also includes express lanes that are separated by a concrete median barrier. These lanes are located between the GPLs northbound and southbound. The median barriers are moveable to manage congestion during peak hours. The standard lane configuration is two northbound lanes and two southbound lanes. This configuration can be changed to three south- bound lanes and one northbound lane to mediate peak-hour traffic demand. Currently, these are the only two-lane configuration choices available. The managed lane facility operates as HOT lanes using distance-based dynamic pricing. Motorcycles and all vehicles with two or more occupants can access the express lanes with no charge. SOVs also are allowed to access the express lanes, but pay a fee. Heavy vehicles (Class 4 and above) are restricted from the express lane facility. Designated ramp entrances and exits to this facility exist to and from SR-163, the I-15 south GPLs, and the I-15 north GPLs. There are two entrances and two exit flyover access ramps near the center of the express lanes facility granting direct access to and from SR-56. There are six access points each in the northbound and southbound directions between the express lanes and the GPLs. The simulation case study site was developed and calibrated using Aimsun microsimulation software. This software allows external API-based control of simulation components, including driver behavior, making it a good candidate for CAV modeling. Driver behavior was calibrated to replicate field-observed corridor travel time, speed, and traffic volume. Roadway speed and volume data were available through the Caltrans Performance Measurement System (PeMS) by specific days with precision of 1-minute intervals (Caltrans 2018). The detector data also classi- fies the traffic volume by lanes. 4.1.4 I-35E MnPass Lanes, St. Paul, Minnesota The I-35 MnPASS lanes that pass through the dense urban area of St. Paul, Minnesota, also represent conditions for assessing feasibility of dedicating lanes to CAVs. On the northern half of the 15-mile study corridor, the managed lane freeway transitions to suburban and rural struc- tures. The freeway corridor also contains a system-to-system interchange with I-694 where the two freeways run concurrently for approximately 1 mile. This facility is shown in Figure 4.6. Existing calibrated models in both CORSIM and Vissim formats are owned by the Minnesota Department of Transportation (Minnesota DOT). Traffic count and speed data detection by lane is archived daily. For this study, traffic count and speed data along the ramps and mainline were obtained through Minnesota DOT’s Regional Traffic Management Center detector data (Minnesota DOT 2017). Turning-movement counts at the ramp terminals, which were available from previous signal retiming projects at most of the study area interchanges, also were used in the model. This corridor experiences typical a.m. and p.m. peak-hour commuter demand and mild to moderate congestion, with the southbound traffic experiencing heavier demand during the a.m. peak and the northbound traffic experiencing greater demand during the p.m. peak Outside of these commuter rush hours, demand drops off considerably, and traffic moves under free-flow conditions. Alternate arterial routes exist (i.e., US-61), and even the regional freeway network provides alternate routes; however, these regional routes were not included in the scope of this microsimulation project. Traffic on the corridor is largely commuter traffic and mostly consists

52 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles of passenger vehicles with a small proportion made up of commercial trucks. On this corridor, transit is not significant enough to affect operations greatly; therefore, transit was not explicitly modeled. Existing ITS strategies include VMS, ramp metering, and traffic speed/volume detec- tors feeding to the traffic management centers. The existing managed lane facility is present on the southern half of the study corridor (south of I-694) and consists of a single lane in each direction. Studies are being performed to assess the feasibility of expanding the current facility to the north. HOV, transit vehicles, and motorcycles can use the current facility at no charge, whereas SOVs pay to use the facility. Large commercial vehicles (with more than two axles and weighing more than 26,000 pounds) are restricted from the managed lane during peak hours but can use the managed lane during non-peak travel times. The facility is currently operated as a managed lane during commuter rush and as a GPL during off-peak hours. A solid double white line indicates that access to the managed lane is restricted. Frequent access areas, which also are major weaving areas, are indicated with striping. Buses are permitted to run along the shoulders of I-35E in the northbound and southbound direction for an approximate 2-mile stretch north of I-694 and an approximate 3-mile stretch south of I-694. Buses can use the outside shoulder along these stretches of I-35E when congestion slows travel speeds to 35 mph or slower. Buses using the shoulder may only exceed adjacent general traffic speeds by 15 mph. Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Legend Study Area Interchange Figure 4.6. I-35E case study site coverage.

Case Study Site Selection 53 Driver behavior was calibrated in CORSIM per Minnesota DOT guidelines. Vissim models were calibrated as well to replicate existing travel speeds and congestion levels during the a.m. and p.m. peak-hour rush periods. 4.1.5 I-94 Managed Lanes (Proposed), Minneapolis, MN The proposed I-94 managed lane facility will be in a dense urban area between Minneapo- lis and St. Paul, Minnesota, and will include system interchanges with I-35W and I-35E (see Figure 4.7). The length of the facility included in the simulation model is approximately 14 miles, extending from I-394 on the west to US-61 on the east, with 32 interchanges modeled. Traffic on the existing corridor is largely commuter traffic and mostly is made up of passenger vehicles with a smaller proportion of commercial trucks. Transit is not a significant enough component of this corridor to impact operations greatly; therefore, transit was not explicitly modeled. The corridor experiences typical a.m. and p.m. peak-hour commuter demand and moderate to heavy congestion during these peak periods. Alternate arterial routes are available with lim- ited river crossings, but the alternate routes were not included in the microsimulation modeling of this project. The facility currently has VMS, ramp metering, and traffic speed/volume detec- tors. The proposed project is to construct an expansion of the managed lane (MnPASS) system to include this corridor. The managed lane would be a single lane in each direction. Buses would be permitted to run along the shoulders of I-94 between Highway 280 and Downtown St. Paul. As with other bus shoulder-running applications in the area, buses would be permitted to use the outside shoulder when congestion slows travel speeds to 35 mph or slower, with bus speeds limited to no more than 15 mph faster than the adjacent general-purpose traffic. The proposed facility would be operated as a HOT lane, allowing free access to high-occupancy passenger cars, transit vehicles, and motorcycles. SOVs would be able to access this facility with a fee. Heavy vehicles are restricted from access to the facility. The proposed operating rules involve time-of-day plans, operating each managed lane as a mixed-use lane during off-peak hours while operating it as a HOT lane during peak periods. Driver behavior was calibrated in CORSIM per Minnesota DOT guidelines. Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.7. I-94 case study site coverage.

54 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles The existing calibrated models were in CORSIM format and owned by the Minnesota DOT, which presented significant challenges to modeling CAV behavior due to limitations in uti- lizing external API to code varying driver behaviors. Traffic count and speed data along the ramps and mainline were obtained through Minnesota DOT’s Regional Traffic Management Center detector data (Minnesota DOT 2017). Traffic count detection is by lane and is archived daily. Turning-movement counts at the ramp terminals were available from previous signal reti- ming projects at most of the study area interchanges. At interchanges where turning-movement counts were not available, new turning-movement counts were collected. 4.1.6 I-290 Managed Lanes, Chicago, Illinois The I-290 facility runs through a dense urban area in Chicago and metropolitan communi- ties to the west of downtown Chicago (see Figure 4.8). A managed lane facility, which includes a single lane in each direction, is proposed on this corridor. The length of the facility included in the simulation model is approximately 14.5 miles, extending from I-294 on the west to I-90 on the east, with 21 interchanges modeled. The corridor experiences typical a.m. and p.m. peak- hour commuter demand and heavy congestion during these 2- to 3-hour peak periods. Outside of these peaks, traffic demand reduces enough to allow for free-flow operations along I-290. There are no restrictions on transit or heavy vehicles on the existing facility and, due to left-side entrance/exit ramps along the corridor, commercial heavy vehicles can utilize all lanes. Alternate arterial routes are available (Roosevelt Road being the primary alternate route), but the alternate routes were not included in the microsimulation modeling of this project. Traffic on the I-290 case study corridor is largely commuter traffic, consisting mostly of pas- senger vehicles with a smaller proportion of commercial trucks. Transit is not a significant com- ponent of this modeled corridor. Commuter rail is present, running immediately adjacent to Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.8. I-290 case study site coverage.

Case Study Site Selection 55 I-290 and within the I-290 median for the eastern half of the study corridor; however, due to limited interaction with the freeway, transit—including commuter rail—was not included in the microsimulation modeling. VMS are present indicating travel time along the corridor. Ramp metering, closed-circuit television (CCTV), and traffic speed/volume detectors also are present on the corridor. The operating rules proposed for this managed lane facility are HOV and HOT time-of-day restrictions by which the facility operates as a managed lane during peak hours and as a GPL during off-peak hours. Access to and from the managed lane is proposed to be indicated using dashed white line pavement striping only, and restricted (no) access is to be indicated with a solid double white line. Existing calibrated models in the Vissim model format were owned by the Illinois Department of Transportation (Illinois DOT). Traffic count and speed data along the ramps and mainline were used for calibration of the models and obtained from the Illinois DOT’s detector database. Traffic count detection is by lane and is archived daily. Turning-movement counts at the ramp terminals were collected at most of the study area interchanges as part of the project. 4.1.7 I-75 HOV Lanes, Detroit, Michigan The I-75 freeway corridor is based in a dense urban area immediately north of the Detroit city limits. This area includes a major system interchange with I-696. The length of the facility included in the simulation model is approximately 6 miles, extending from M-102 on the south end to 12 Mile Road on the north end, with five interchanges included (see Figure 4.9). The microsimula- tion model was prepared for detailed analysis of a subarea of a larger (18.5-mile) corridor being studied for the addition of a managed lane (from M-102 on the south end to M-59 on the north end). The corridor experiences typical a.m. (southbound) and p.m. (northbound) peak-hour commuter demand and moderate congestion during these peak periods. Outside of these peaks, traffic demand reduces enough to allow for free-flow operations along I-75 during most of the day. A managed lane facility with a single lane in each direction was proposed for immediate construction. The construction would require widening along the corridor for the addition of this lane in each direction. The proposed managed lane facility would extend 12.5 miles, from SR-59 to approximately 12 Mile Road. This would be the first managed lane facility along the freeway system in Michigan. Operational rules for this managed lane facility would be based on a time-of-day HOV restriction, by which the facility would operate as a managed lane during peak hours and as a GPL during off-peak hours. Alternate arterial routes are available, with Woodward Avenue being the primary alternate route; however, the alternate routes were not included in the microsimulation modeling of this project. Traffic on this corridor is largely commuter traffic, consisting mostly of passenger vehicles with a smaller proportion of commercial trucks. Transit is not a significant component of this modeled corridor. Existing ITS strategies and technologies along the corridor include VMS, CCTV, and traffic-count stations. Areas providing access to and from the managed lane are proposed to be indicated by dashed white line striping only. Restricted areas (with no access to or from the managed lane) are pro- posed to be indicated by a solid double white line. The entire corridor (18.5 miles) was given a macroscopic Highway Capacity Manual analysis of the basic freeway segments, merge/diverge areas, and weave areas. A more detailed micro- simulation analysis was conducted for a 7-mile section containing the system interchange with I-696 and proposed ramp-braiding alternatives. The microsimulation was conducted in Vissim (Version 6), and traffic count data was obtained from the Michigan Department of

56 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Transportation (Michigan DOT) traffic count web portal for ramps and mainline counts along the corridor (Michigan DOT 2018). The model was set up as a ramp and mainline model only. Full interchange operations were not modeled. Speed and congestion data used for calibration were obtained from the Regional Integrated Transportation Information System (RITIS) maintained by the University of Maryland (CATT Lab 2018). 4.1.8 I-270 Corridor, Maryland I-270 is a 34.7-mile auxiliary Interstate Highway that travels between I-495 (the Capital Belt- way) just north of Bethesda, in Montgomery County, Maryland, and I-70 in the city of Frederick in Frederick County, Maryland. The corridor consists of a 32.60-mile main line plus a 2.10-mile spur that provides access to and from southbound I-495 (see Figure 4.10). Most of the southern part of the route in Montgomery County passes through suburban areas around Rockville and Gaithersburg. This portion of I-270 is up to 12 lanes wide and consists of a local-express lane configuration as well as HOV lanes that are in operation during peak travel times. North of the Gaithersburg area, the road continues through the northern part of Montgomery County as a 6- to 8-lane highway with an HOV lane in the northbound direction only. Farther north, I-270 continues through rural areas into Frederick County and toward the city of Frederick as a 4-lane freeway. The modeled length is approximately 26 miles. Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.9. I-75 case study site coverage.

Case Study Site Selection 57 This corridor experiences very little diversity in demand conditions and traffic patterns, and currently operates at a high level of congestion throughout most typical days. The only parallel alternate route is SR-355, an arterial corridor with signalized intersections and significant busi- ness activity. Modes of transportation included in the model were passenger cars, buses, and heavy vehicles. The parallel alternative routes were not included in the model. The model included imme- diate facilities, such as highway interchanges and immediate signalized intersections at the interchanges. The current ITS infrastructure consists of speed detectors, video monitoring infrastructure, and VMS. The Maryland DOT is in the process of procuring an innovative congestion-management upgrade project ($100 million) that could introduce a range of new technologies/strategies. The facility’s managed lane is currently a single HOV lane in each direction. HOV operating restrictions apply during the traffic peak period in the peak direction only. The HOV lane is concurrent with other lanes and is distinguished by special pavement markings. Vehicles can access the managed lane from the GPL throughout the entire facility with no restrictions. The simulation platform used to develop the model was Vissim. No existing ITS strategies were included in the existing model. 4.1.9 I-95 Express Lanes, Miami, Florida Interstate 95 (I-95) is a key component of the Interstate Highway System, running along the east coast of the country from Miami, Florida, to the U.S.-Canada border in eastern Maine. The study segment of this facility is an urban freeway with directional commuter traffic flows that runs through the densely urban cores within Miami–Dade and Broward Counties in South Florida Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.10. I-270 case study site coverage.

58 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles (see Figure 4.11). The managed lane section currently runs approximately 20 miles between Davie Road, near Downtown Ft. Lauderdale, to just north of Downtown Miami at SR-836. I-95 operates at high levels of congestion throughout most of the day, with concentrated congestion at various bottlenecks during off-peak hours while distributed throughout the facility during peak periods. The closest parallel facility is the signalized arterial of US-1/ Federal Highway/Biscayne Boulevard. Another parallel alternative route located along the northern portion of the facility is the Florida Turnpike. The various transportation modes include passenger cars, heavy vehicles, and buses. Heavy vehicles are restricted from using the express lanes. Existing ITS strategies used along the I-95 corridor include VMS, video monitoring, and various detectors. The I-95 express lanes represent a conversion from HOV to HOT operation and were imple- mented to provide more reliable trip times for corridor users. The facility allows for toll-free access for HOV3+ users and transit but requires carpools to pre-register. SOVs can access the lane by paying a toll that is assessed on a dynamic basis in response to congestion. As volumes increase, so does the price for access. Currently, the maximum rate for an SOV is $1.50 per mile or $10.50 over the full length of the express lanes. This cap may be raised if the LOS on the facility Source: NCHRP 20-102(08) project team; base map from Florida DOT (www.95express.com). Figure 4.11. I-95 case study coverage.

Case Study Site Selection 59 consistently declines below 45 mph over a 90-day period, a policy that is largely the result of the project’s initial funding through the Federal Urban Partnership Agreement. The Florida DOT estimates that about 2% to 3% of the traffic in the express lanes is travelling toll free. The managed lanes are separated from the GPLs by flexible delineator posts. The model cur- rently includes four northbound entrances, five southbound entrances, four northbound exits, and four southbound exits between the managed lanes and GPLs (see Figure 4.12). The simulation platform used to develop this network was Vissim. The Vissim modeling included the GPLs, the managed lanes, and the individual interchange operations. 4.2 Evaluation Criteria This section describes the evaluation criteria used for modeling the initial candidate case study sites. These evaluation criteria were ranked as being of low, medium, or high priority based on their relevancy in assessing DL conditions. For example, model availability is considered a high-priority criterion, whereas having a moderately sized facility is of low priority. Based on the relative importance of each evaluation factor, the team used weighted scoring when ranking case study sites. 4.2.1 Case Study Site Characteristics The team used eight evaluation criteria to identify characteristics and rank the case study sites. This evaluation included characterization of the geographic and operational conditions that exist in the test sites. 4.2.1.1 Geographic Characteristics Managed lanes generally are an urban/suburban roadway feature; hence, it is desirable that the final selected case study sites represent reasonable use of dedicated/managed lanes in or near metropolitan area conditions. Drivers in larger metropolitan areas will be more accustomed to regularly encountering recurring or nonrecurring congestion. In larger metro politan areas, congestion will tend to be more ubiquitous and bidirectional. For this criterion, the characteristics assessed reflected diverse sites that ranged from less urban to more urban in terms of number of lanes, AADT, and location. This evaluation criterion was given medium priority in the case study selection process because managed lanes are mostly an urban feature (Figure 4.13). 4.2.1.2 Availability of Data/Case Study Site Model Successful modeling of CACC in DLs depends on the model’s closeness to the real world. Hence, availability of a calibrated case study site model is of extreme importance. The team selected case study site models that were available for use in a calibrated state. To evaluate the impacts of DLs for CACC-equipped vehicles, the case study sites needed to be validated and cali- brated using historical, near real-time, and real-time data. The data had to represent a case study site’s geographic and temporal scope as well as characteristics such as existing ITS infrastructure and managed lane configurations. The availability of case study models and the associated cali- bration data was a high-priority evaluation criterion, given the importance of a fully calibrated simulation model in assessing realistic and credible benefits and sensitivity parameters of CACC application on a DL facility. The research team gave preference to models that were available in an open-source portal such as the U.S.DOT’s Open Source Application Development Portal (OSADP) or the U.S.DOT Data Repository, as well as models that were available upon request from local agencies (see Figure 4.14).

60 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Source: NCHRP 20-102(08) project team; base map data from Florida DOT (2018). Figure 4.12. I-95 express lane configurations and access points.

Case Study Site Selection 61 Figure 4.14. Case study characterization based on model availability. Figure 4.13. Case study characterization based on geographic characteristics. 4.2.1.3 Diversity in Demand/Operational Conditions Operating demand of a corridor facility determines the operational conditions for the drivers. For this case study selection, we assess the demand in terms of traffic volumes over the entire case study site. Traffic demand for low (uncongested), medium (near capacity), and high (congested) levels will yield different traffic patterns and a wide range of cases to assess and compare their performances. Although low-demand conditions do not present challenging conditions for the deployment of CAV applications, having a variable demand would allow assessment of impacts under different saturation rates. Hence, the selection included a case study site with varying traffic demand, or multiple case study sites that represent different demands. Having differing demand conditions is important to analyze the sensitivity of DLs under various saturation rates, but the demand can be scaled easily from existing models. Therefore, this criterion was consid- ered medium priority (see Figure 4.15). 4.2.1.4 Length of Facility The length of a DL facility relative to the overall case study site is an important factor in gaug- ing its influence on the overall network, parallel corridor, and parallel arterials. The length of the facility corresponds directly to the proportion of benefits or disbenefits imposed on the assess- ment boundary, which is defined as the limits of the roadway facility that have been included in the assessment. Effects like the proportion of a given trip utilizing the DL versus not using the DL can be compared between facilities with longer DLs versus shorter DLs. At the same time, modeling the CACC application entails computationally intensive driver-behavior capture to an external interface and trajectory implementation, and larger models can become difficult to Figure 4.15. Case study characterization based on operational demand.

62 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Figure 4.16. Case study characterization based on routing features. model. The team selected medium-sized facilities to enable full evaluation of trip-based perfor- mance measures as well as manage the computation size. This evaluation criterion was given a medium priority in the case study selection. 4.2.1.5 Availability of Alternate Routes One consideration in assessing CACC DLs’ impact on non-users is the availability of alternate routes, such as parallel arterials. The case study sites were assessed to determine whether a parallel route was explicitly included in the model and whether vehicle rerouting was possible through these alternate routes. For the case study selection, the team prioritized models that included alternate routes. Because most of the candidate models had been developed for corridor analysis, however, only a few might have included alternate parallel routes. This evaluation criterion was given a medium priority in the case study selection due to this limitation (see Figure 4.16). 4.2.1.6 Diversity in Modes Varying modes of transportation within the traffic stream composition is an important consideration due to its impact on traffic flow characteristics. Freeways and interstates in urban environments have a significant composition of heavy and transit vehicles unless heavy and/or transit vehicle access is restricted. Heavy and transit vehicles have different accel- eration and deceleration profiles compared to passenger cars. For evaluation purposes, the selected case study sites needed to have varying vehicle type composition or be restricted by time-of-day access to heavy and transit vehicles so that their impacts could be assessed. This evaluation criterion was given a medium priority in the case study selection (see Figure 4.17). 4.2.1.7 Existence of Managed Lanes Managed lanes commonly are used within urban metropolitan areas. Types of managed lanes may include HOV lanes, HOT lanes, and express toll lanes (ETLs). One important factor for consideration is the traffic and safety impacts of using several types of managed lanes on the same corridor. Other impacts include mixed use of managed lanes, which may include dedicated CACC with HOV, HOT, and ETLs. These scenarios could be compared to a scenario with com- plete conversion of existing managed lanes to dedicated CACC lanes. For this study, the research team gave preference to case study sites with existing managed lanes or where managed lanes had been proposed for deployment in the near future. This evaluation criterion was given a medium priority in the case study selection (see Figure 4.18). Figure 4.17. Case study characterization based on modal diversity.

Case Study Site Selection 63 4.2.1.8 Existence of ITS Strategies ITS strategies are implemented to maximize roadway carrying capacity and increase safety. Concurrent implementation of ITS strategies with CACC DLs may have either synergistic or conflicting effects on roadway capacity and driver safety. For example, CACC is expected to work synergistically with dynamic speed limits because it improves the string stability of CACC platoons. The research team assessed the case study sites to determine whether ITS strategies existed and were modeled in the available simulation model. These existing ITS strategies could then be screened for conditions that can cause synergies or conflicts with CACC applications. This evaluation criterion was given a low priority in the case study site selection because currently implemented ITS strategies may or may not exist in conjunction with CACC implementation. 4.2.2 Managed Lane Characteristics The existing or proposed characteristics of the managed lanes for each of the case study sites also were assessed. Specifically, the following five characteristics were used for case study site scoring: managed lane geometry, user types, operating rules, physical barrier types, and diversity in access point configurations. 4.2.2.1 Managed Lane Geometry The number of lanes available for use as managed lanes is a critical factor to assess the capacity benefits of additional lanes. Capacity impacts are an important determining factor in deciding on the implementation of additional lanes due to roadway widening or hard running shoulder uses. Addi- tional lanes mitigate the “snail” effect by which the slowest-moving vehicle in the managed lane can govern the speed of the entire lane. Additional lane design should complement the access manage- ment strategy to accommodate traffic safety and capacity due to lane changes. Case study sites with a diverse number of DLs and varying roadway geometries were preferred so that these impacts could be assessed. This evaluation criterion was given a low priority in the case study selection. 4.2.2.2 User Types Within the selected case study sites, existing managed lanes (e.g., HOT lanes, HOV lanes, and ETLs) with a mix of user-types (e.g., SOVs, HOVs, transit vehicles, and heavy vehicles) were pre- ferred. To assess the benefits and disbenefits of imposing future restrictions on current user types (e.g., through conversion of existing managed lanes to dedicated CAV lanes) the team identified case study sites with a diverse user base. Among the project objectives, a major consideration was to evaluate the feasibility of mixed lane use by CAV vehicles and non-CAV vehicles. Hence, this evaluation criterion was given a medium priority in the case study selection (see Figure 4.19). 4.2.2.3 Operating Rules Managed lanes can have a variety of operating rules to manage the facility for both operational and safety reasons. For example, time-of-day and vehicle-class access restrictions commonly are used along certain managed lane facilities. These operating rules influence traffic patterns Figure 4.18. Case study characterization based on managed lanes.

64 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles throughout the day at the imposed area. Other operating rules may include the enforcement of left-lane passing only laws, which may involve safety concerns for vehicles that must pass mul- tiple platooned vehicles to find an acceptable gap for a lane change. The team categorized the testbed operating rules as: a. Time-of-day operation, wherein lanes operate as managed lanes only during peak hours. During non-peak hours, no lanes are dedicated to special vehicle categories such as HOVs or toll-paying SOVs. b. Time-of-day pricing, wherein lanes always operate as managed lanes, but the pricing depends on the time of day and follows a schedule. This category includes managed lanes for which off-peak usage may be free. c. Dynamic congestion pricing, wherein the usage fee for the managed lanes is determined based on existing travel conditions. This evaluation criterion was given a medium priority in the case study selection because the variance in these factors is somewhat limited (see Figure 4.20). 4.2.2.4 Physical Barrier Types Managed lanes that are separated from the GPLs by physical barriers like flexible delineator posts or concrete median barriers may have different posted speed limits from the GPLs. The potential difference in speed limits distinguishes managed lanes with physical barriers from managed lanes that are separated only by pavement striping. Differences in posted speed limits will have considerable effects on roadway capacity, traffic characteristics, and driver behaviors. The impacts on driver behaviors and traffic characteristics caused by varying physical barrier separations can be assessed and compared to the impacts on driver behaviors and traffic charac- teristics at managed lanes with no physical barrier separations. Accordingly, the research team gave preference to a testbed portfolio that included varying barrier types. This evaluation crite- rion was given a medium priority in the case study selection (see Figure 4.21). 4.2.2.5 Diversity in Access Point Configurations Access points to and from the DLs have a significant impact on the roadway capacity. The frequency of available access points along a DL directly correlates to drivers’ wayfinding and Figure 4.19. Case study characterization based on managed lane user characteristics. Figure 4.20. Case study characterization based on managed lane operating rules.

Case Study Site Selection 65 access to the facility. Driver lane-changing behaviors on both GPLs and DLs will be affected by advanced knowledge of access availability. Treatments that mediate the impacts of traffic turbu- lence caused by weaving vehicles making lane changes to enter or exit the DLs also are impor- tant factors to consider. The two types of access point configurations categorized by the team were continuous access and restricted access, with the latter type defined by access point frequency, strictness of access point location, and access section length. Access point frequency and weave management treatments, such as shorter access lengths (which challenge weaving movements), could be compared to assess the treatments’ impacts on both the GPLs and DLs. This evaluation criterion was given a medium priority in the case study selection (see Figure 4.22). 4.2.3 CAV Modeling Feasibility The feasibility of modeling CAVs also represented an important set of scoring criteria. Spe- cifically, the case study site needed to be modeled in an environment that permitted model- ing of customized vehicle and driver behavior. Specific feasibility criteria considered by the research team were the possibility of external programming interface and available driver behavior calibration data. 4.2.3.1 Possibility of External Programming Interface For the purposes of this project, the simulation environment needed to allow for modeling CACC driver and automatic car-following behaviors. The environment needed to allow for the inclusion of external API or a software-in-the-loop-system, if the CACC driver behavior was not already readily available with the model. External API also was required to query and receive vehicle parameters that were not already catalogued for analysis. This evaluation criterion was given a high priority in the case study selection because modeling CACC applications without external API was not possible (see Figure 4.23). 4.2.3.2 Available Driver Behavior Calibration Data The case study sites selected would need driver behavior calibration data specific to the local environments to allow for a detailed replication of conventional local driving behavior. A case study model that closely mimicked existing driver behavior would provide for a high-fidelity rep- resentation of the case study area and better comparisons among analyzed scenarios. Assessing Figure 4.21. Case study characterization based on managed lane separation. Figure 4.22. Case study characterization based on managed lane access features.

66 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles the traffic flow performance under a mixed-use case including CAV and non-CAV was critical to determining their traffic impacts, so this criterion was given a high priority. 4.3 Selected Case Study Sites The project team used the scoring criteria discussed in the preceding sections of this chapter to score and rank the nine candidate case study sites. 4.3.1 Case Study Site Scoring The study team developed a comprehensive scoring process to rank the initial candidate case study sites and select a portfolio of case study sites that could be used to effectively model the CAV applications and determine the implications on dedicating lanes to such vehicles. The selected case study sites needed to be able to define guidelines that agencies can use to determine whether their specific applications would merit lane dedication. These guidelines should include different levels of traffic congestion, network connectivity, availability of alternate routes and modes, spacing of access/egress points, truck traffic, and traffic patterns (e.g., core focused versus dispersed). Selecting a single case study site would not be sufficient to model the diversity in conditions that needed to be assessed, whereas modeling numerous test sites would be resource-intensive. Consequently, the team used the evaluation criteria scoring process to select two case study sites. 4.3.1.1 Mapping of Evaluation Criteria The team used the evaluation criteria it had developed to identify a set of 15 parameters (see Table 4.2). The nine initial candidate testbeds were then evaluated based on these 15 parameters. For each parameter, the team identified corresponding site-specific value(s), which are shown in Table 4.2 and Table 4.3. Multiple values were selected for certain parameters that involved a mix of different values. For example, the case study from St. Paul, Minnesota involved a mix of rural, suburban, and urban geographical areas. 4.3.1.2 Scoring of Case Study Sites Once the site-specific value for each parameter had been assessed, the team scored the param- eter based on whether it was least preferred (0) to most preferred (3), as shown in Table 4.4. For example, for availability of model and data, the Northern Virginia test site received a score of 3 because the model is available as open source, whereas case study sites such as the Chicago site received a score of 2. A weighted factor to indicate the priority of that specific evaluation factor was assigned. A weight value of 1 through 3 was used for factors with priority low to high, respectively. The final score of each testbed was calculated as a sum-product of each of the evaluation scores and their corresponding weights (w). Thus, for a testbed (i), the final score (Si) was calculated as follows: ∑= ,S s wi ij jj where j represents the variable evaluation scores. Figure 4.23. Case study characterization based on modeling interface.

Northern Virginia San Mateo, California San Diego, California St. Paul, Minnesota Minneapolis, Minnesota Chicago, Illinois Detroit, Michigan Maryland Miami, Florida Ca se S tu dy S ite C ha ra ct er is tic s Characteristics Urban ● ● ● ● ● ● Suburban ● ● ● ● ● Rural ● Availability of Model and Data Open-source ● ● Available on Request ● ● ● ● ● ● ● Unavailable Demand Levels Low ● ● ● ● ● Medium ● ● ● ● ● ● ● High ● ● ● ● ● ● ● ● ● Size of Model Length (Miles) 13 8.5 22 15 14 14.5 18.5 26 20 Alternate Routing Unavailable Available, but not modeled ● ● ● ● ● ● ● Available and modeled ● ● Modal Diversity Cars ● ● ● ● ● ● ● ● ● Trucks ● ● ● ● ● ● ● ● ● Transit ● ● ● Existing ITS Strategies None Available ● ● ● ● ● ● ● ● ● Managed Lanes Existing ● ● ● ● ● Proposed ● ● ● ● Unavailable Table 4.2. Modeling feasibility given site-specific values for case study site and managed lane characteristics (Part 1 of 2).

Northern Virginia San Mateo, California San Diego, California St. Paul, Minnesota Minneapolis, Minnesota Chicago, Illinois Detroit, Michigan Maryland Miami, Florida M an ag ed L an e Ch ar ac te ri sti cs Characteristics Number of Lanes 1 2 2 1 1 1 1 1 2 User Types HOV ● ● ● ● ● ● ● ● ● HOT ● ● ● ● ● Transit ● ● ● ● ● ● ● ● ● Trucks ● Operating Rules Time of Day Operation ● ● ● ● ● ● ● ● Time of Day Pricing Congestion Pricing ● ● ● Physical Barriers None ● Lane-marking ● ● ● ● ● ● Delineators ● ● Separated Access Point Throughout ● ● Limited ● ● ● ● ● ● ● Fe as ib ili ty Modeling Platform API Unavailable ● API Available ● ● ● ● ● ● ● ● Driver Behavior Not Calibrated Calibrated ● ● ● ● ● ● ● ● ● Table 4.3. Modeling feasibility given site-specific values for case study site and managed lane characteristics (Part 2 of 2).

Case Study Site Selection 69 Parameter Scoring and Criteria 1. Case Study Characteristics Geographic Characteristics 3 = Urban region. 2 = Suburban region. 1 = Rural region. Availability of Model and Data 3 = All models that were available as open-source. 2 or 1 = The score was lowered based on the increasing difficulty of obtaining the model. Demand Levels 3 = Sites that replicate low, medium, and high demand conditions. 2 or 1 = The score was lowered depending on the model’s inability to mimic certain demand conditions. Size of Model 3 = Sites between 7 miles and 14 miles in length. 2 or 1 = The score was lowered for smaller or larger sites owing to the relative increase in complexity/computational intensity of modeling CAV applications at these sites. Alternate Routing 3 = Sites with an available alternate route that also could be modeled. 2 or 1 = The score was lowered when an alternate route was not available for modeling. Modal Diversity 3 = Sites with a diverse modal set (including cars, trucks, and transit). 2 or 1 = The score was lowered when the number of modes was reduced. Existing ITS Strategies 3 = Sites with existing ITS strategies (e.g., ramp metering, hard-shoulder running, variable speed limits). 1 = Sites without existing ITS strategies.* 2. Managed Lane Characteristics Existence of Managed Lanes 3 = Sites with existing managed lanes. 2 = Sites with proposed managed lanes. 1 = Sites with no managed lanes. User Types 3 = Sites that allow all types of users in the managed lanes. 2 = Sites with some restrictions on vehicle types allowed in the managed lanes. 1 = Sites with the greatest restrictions on vehicle types allowed in the managed lanes. Operating Rules 3 = Sites with an operating rule. 1 = Sites without an operating rule.* Physical Barriers 3 = Sites with separation or barriers. 1 = Sites without separation or barriers.* Access Options 3 = Sites with limited-entry managed lanes. 1 = Sites with continuous access.* 3. CAV Modeling Feasibility CAV Modeling Ability 3 = Sites with available API. 1 = Sites without available API.* Driver Behavior 3 = Sites that can be calibrated to realistic driving behavior. 1 = Sites not calibrated to realistic driving behavior.* *Scoring for this parameter did not include a score of 2. Table 4.4. Case study site scoring criteria.

70 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles With regard to parameters for which variety was preferred, the case study sites that represented a diverse set of values were given higher scores. For example, St. Paul, Minnesota, received a high score for demand levels because the case study site is subject to varying demand levels. These scores were generated for each parameter and a total score was assessed as shown in Table 4.5. Based on the case study site scores provided in Table 4.5, the top-ranking testbeds were: 1. Northern Virginia, and 2. San Mateo, California. Chapter 5 provides a detailed description of these two testbeds along with details on their calibration data and operational conditions in terms of traffic demand, weather conditions, and occurrence of incidents. W ei gh ts N or th er n Vi rg in ia Sa n M at eo , Ca lif or ni a Sa n Di eg o, Ca lif or ni a St . P au l, M in ne so ta M in ne ap ol is, M in ne so ta Ch ic ag o, Ill in oi s De tr oi t, M ic hi ga n M ar yl an d M ia m i, Fl or id a Ca se S tu dy S ite C ha ra ct er isti cs Geographic Characteristics 2 2 3 2 3 3 3 3 2 3 Availability of Model and Data 3 3 3 2 2 2 2 2 2 2 Demand Levels 2 3 3 2 3 3 3 3 1 1 Size of Model 2 3 3 2 2 3 2 2 1 2 Alternate Routing 2 2 3 3 2 2 2 2 2 2 Modal Diversity 2 3 3 3 2 2 2 2 2 2 Existing ITS Strategies 1 3 3 3 3 3 3 3 3 3 M an ag ed L an e Ch ar ac te ris tic s Existence of Managed Lanes 2 3 2 3 3 2 2 2 3 3 User Types 2 2 2 3 3 3 3 2 2 2 Operating Rules 2 3 3 3 3 3 3 3 3 3 Physical Barriers 2 3 3 3 3 3 3 3 3 3 Access Options 2 3 1 3 3 3 3 1 1 3 M od el in g Fe as ib ili ty CAV Modeling Ability 3 3 3 3 3 1 3 3 3 3 Driver Behavior 3 3 3 3 3 3 3 3 3 3 TOTAL CASE STUDY SITE SCORE 84 82 81 81 75 79 73 67 75 Table 4.5. Site-specific scoring matrix.

TRB’s National Cooperative Highway Research Program (NCHRP) Research Report 891: Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles identifies and evaluates opportunities, constraints, and guiding principles for implementing dedicated lanes for connected and automated vehicles. This report describes conditions amenable to dedicating lanes for users of these vehicles and develops the necessary guidance to deploy them in a safe and efficient manner. This analysis helps identify potential impacts associated with various conditions affecting lane dedication, market penetration, evolving technology, and changing demand.

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The case study approach

Sarah crowe.

1 Division of Primary Care, The University of Nottingham, Nottingham, UK

Kathrin Cresswell

2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Ann Robertson

3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Anthony Avery

Aziz sheikh.

The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table ​ (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Definitions of a case study

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table ​ (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table ​ (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables ​ Tables2 2 and ​ and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table ​ (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table ​ (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

Example of epistemological approaches that may be used in case study research

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table ​ Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table ​ (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table ​ Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table ​ (Table2 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table ​ (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table ​ (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table ​ (Table4 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table ​ Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table ​ (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table ​ Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 8 ]

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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Elon Musk's Big Bets

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Tesla's CEO Compensation Plan

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China Rapid Finance: The Collapse of China's P2P Lending Industry

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Forbidden City: Launching a Craft Beer in China

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Booking.com

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Innovation at Uber: The Launch of Express POOL

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GitLab and the Future of All-Remote Work (A)

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AT&T, Retraining, and the Workforce of Tomorrow

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The Great East Japan Earthquake (B): Fast Retailing Group's Response

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Alex Montana at ESH Manufacturing Co.

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Additional information about International Student Program reforms

Ottawa, February 5, 2024— Further information is being provided to clarify the announcement of an intake cap on new international study permit applications and other changes . International students make important contributions to Canada’s campuses, communities and economy; however, we have seen unsustainable growth in the International Student Program in recent years. These recently announced reforms will support sustainable population growth in Canada and improve system integrity, while helping to ensure that international students have a positive experience in Canada.

1. Cap and provincial attestation letter

As of 8:30 a.m. ET on January 22, 2024, most new post-secondary international students at the college or undergraduate level must provide a provincial attestation letter (PAL) from a province or territory with their study permit application. Immigration, Refugees and Citizenship Canada (IRCC) will return any application received that does not include a PAL, unless otherwise exempt.

This attestation will serve as proof that the student has been accounted for under a provincial or territorial allocation within the national cap. Provinces and territories have been asked to have a plan in place for issuing PALs by March 31, 2024. The Government of Canada is working with the Government of Quebec to determine how the certificat d’acceptation du Québec pour études could serve as a PAL.

International students whose applications were received by IRCC before 8:30 a.m. on January 22, 2024, as well as those who have already been approved for a study permit and intend to travel to Canada for an upcoming program, do not need to take further action as a result of the cap.

Who needs a provincial attestation letter?

  • most post-secondary study permit applicants
  • most non-degree granting graduate programs (for example, certificate programs and graduate diplomas)
  • anyone else not included in the exception list below

Who doesn’t need a provincial attestation letter?

  • primary and secondary school students
  • master’s or doctoral degree students
  • visiting or exchange students
  • in-Canada study permit and work permit holders (includes study permit holders applying for an extension)
  • in-Canada family members of study permit or work permit holders
  • students whose application we received before 8:30 a.m. ET on January 22, 2024

2. Post-graduation work permit (PGWP) update for graduates of master’s degree programs

In recognition that graduates of master’s degree granting programs are excellent candidates to succeed in Canada’s labour market and potentially transition to permanent residence, we have made a change to the length of the PGWP, so that they have the opportunity to meet the required Canadian work experience in order to apply for their permanent residence.

Starting on February 15, 2024, a longer, 3-year post-graduation work permit will be available to those who are graduating from a master’s degree program that is less than 2 years and who meet all other PGWP eligibility criteria.

The length of PGWPs for programs other than master’s degrees will continue to align with the length of the study program, to a maximum of 3 years.

Who is eligible for a longer post-graduation work permit (PGWP)?

  • Graduates of programs that are at least two years in length at PGWP-eligible designated learning institutions are eligible for a 3-year PGWP, as are graduates of master’s degree programs less than 2 years in length.

3. PGWP eligibility for public-private partnership college programs

Some provinces allow public colleges to license their curriculum to be delivered by an affiliated private college. In these cases, students physically attend a private college, but graduate with a diploma from a public institution. Concerns have been raised with regard to the quality of education provided by these institutions, as well as the lack of sufficient student supports. The Auditor General of Ontario has also raised concerns about a lack of oversight into program quality and student services at these institutions.

As such, IRCC has made a change to restrict PGWPs for these institutions, anticipating that without the ability to apply for a PGWP, there will be a reduction in the number of international students enrolling in them.

Who is eligible for a PGWP after graduating from a public-private partnership college program?

  • International students currently enrolled will remain eligible for a PGWP if they meet other program eligibility criteria.

Who is not eligible for a PGWP after graduating from a public-private partnership college program?

  • New students enrolling in this type of program will not be eligible for a post-graduation work permit.

4. Changes to open work permit eligibility for spouses

In the coming weeks, eligibility for open work permits for the spouses and common-law partners of international students will be updated.

Who can get an open work permit?

  • Eligibility is limited to the spouses and common-law partners of students in graduate (master’s and doctorate) and professional degree–granting programs only.
  • Once these changes are in effect, spouses and common-law partners of international students seeking to extend their existing work permit will continue to be eligible under this stream.

Who will not be eligible for an open work permit?

  • The spouses and common-law partners of international students in other levels of study, including undergraduate and college programs, will no longer be eligible for an open work permit unless they already hold an open work permit under this stream.

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IMAGES

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    PMID: 28439809 DOI: 10.1208/s12248-017-0083-7 Abstract As the antibody drug conjugate (ADC) community continues to shift towards site-specific conjugation technology, there is a growing need to understand how the site of conjugation impacts the biophysical and biological properties of an ADC.

  3. Case Study Site Selection: Using an Evidence-Based Approach in Health

    Case Study Site Selection: Using an Evidence-Based Approach in Health-Care Settings By: Gail V. Barrington , Rena Shimoni & Augusto V. C. Legaspi Product: Sage Research Methods Cases Part 1 Publisher: SAGE Publications, Ltd. Publication year: 2014 Online pub date: January 01, 2014

  4. Criteria for site selection in industry-sponsored clinical trials: a

    Background Knowledge of what the pharmaceutical industry emphasizes when assessing trial sites during site selection is sparse. A better understanding of this issue can improve the collaboration on clinical trials and increase knowledge of how to attract and retain industry-sponsored trials. Accordingly, we investigated which site-related qualities multinational biopharmaceutical companies and ...

  5. Clinical Trial Site Identification and Selection

    With IQVIA Connected Intelligence, our clinical trial experts can bypass the low performers and instead prioritize the top potential recruiters -- speeding enrollment and your study completion. To validate this approach, we put it to the test against the universe of our >500 ongoing clinical studies. See the impact based on actual enrollment ...

  6. The Importance of Site Selection

    A case study focused on the quest for a potential Parkinson's biomarker highlights the opportunities and challenges in site selection The Michael J. Fox Foundation for Parkinson's Research (MJFF) aims to speed clinical research by removing obstacles that stand in the way of drug development.

  7. Application of choosing by advantages to determine the optimal site for

    15 Citations Metrics Abstract Solar energy is a critical component of the energy development strategy. The site selection for solar power plants has a significant impact on the cost of energy...

  8. The role of place image for business site selection: a research

    The paper posits that brand, visual image, and reputation will have a positive direct effect on place image, and place image will have a positive direct impact on site selection decision. A recent case study of Amazon that provides valuable insights on factors (e.g., place image) that Amazon considered in its site selection for headquarters 2 ...

  9. Comprehensive Review of the Landfill Site Selection ...

    The authors of all reviewed papers propose a landfill siting methodology and conduct their research on a case study. Table 1 presents the country where the case study was carried out, and the frequency/total number of publications per country out of total number of identified 30 case study countries. Iran, Turkey, India, Greece and USA and are the top five countries rated according to the ...

  10. Survey Site Selection Based on the Spatial Sampling Theory: A Case

    A Case Study on Experiment Site Selection for PV Energy Generation Forecast 2020 International Computer Symposium (ICS) 10.1109/ics51289.2020.00098

  11. Case Study Site Selection

    Page 47 Suggested Citation: "Chapter 4 - Case Study Site Selection." National Academies of Sciences, Engineering, and Medicine. 2018. Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/25366. × Save Cancel Page 48

  12. A multi-criteria decision-making framework for site selection of

    The main contribution of the present study is develop a reliable multi-criteria group decision-making framework (that utilises the Bayesian BWM as a decision-making tool), to first establish the complex decision-making problem of site selection in the Australian offshore environment, and second, to solve the problem using a hybrid method ...

  13. Case Study Site Selection: Using an Evidence-Based Approach in Health

    Semantic Scholar extracted view of "Case Study Site Selection: Using an Evidence-Based Approach in Health-Care Settings" by G. Barrington et al.

  14. PDF CASE STUDY: Site Selection

    CASE STUDY: Site Selection company manufacturing automotive aftermarket products was rapidly outgrowing their existing facilities and was looking for an additional location for expansion. Though they manufactured most of their products, some were brought into the country from overseas in containers.

  15. Case Studies Industrial Site Selection

    Case Studies Industrial Site Selection | Colliers Site Selection EXPERIENCE COAST TO COAST This map highlights every engagement we have undertaken. Click on the company logos to uncover the benefits we helped produce for these great clients. SELECT ANY CASE STUDY BELOW TO LEARN MORE

  16. PDF Developing Statistical "Twins" for Qualitative Case Study Site

    Sample Survey Theory and/or Experimental Design in Case Study Site Selection Theory of sample survey chiefly provides estimates of means and totals which are not the goals of case study.

  17. Case Selection Techniques in Case Study Research: A Menu of Qualitative

    Case selection is the primordial task of the case study researcher, for in choosing cases, one also sets out an agenda for studying those cases. This means that case selection and case analysis are inter twined to a much greater extent in case study research than in large-Af cross-case analysis. Indeed, the method of choosing cases and ...

  18. Demographic Mapping & Site Selection Software

    ArcGIS Business Analyst is a demographic mapping software tool for smarter site selection, market planning, customer segmentation, territory design and infographics. ... CASE STUDY. Mid-America Real Estate Group. Using Business Analyst, Mid-America has refined its site selection workflows to identify the best possible locations for its clients ...

  19. The case study approach

    The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. ... For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems ...

  20. HBS Case Selections

    Case Selections HBS Case Selections Get the perspectives and context you need to solve your toughest work problems with these immersive sets of real-world scenarios from Harvard Business...

  21. Case Selection Techniques in Case Study Research: A Menu of Qualitative

    Hence attention to purposive modes of sampling is needed. Yet, while the existing qualitative literature on case selection offers a wide range of suggestions for case selection, most techniques discussed require in-depth familiarity of each case. Seven case selection procedures are considered, each of which facilitates a different strategy for ...

  22. Case Study: Site selection Model for retail stores

    Case Study: Site selection Model for retail stores - Predik Data Case Study: Site selection Model for retail stores Learn how a retail clothing franchise used a predictive model to optimize its expansion strategy and identify the most suitable areas for the opening of new brick-and-mortar stores.

  23. Case Study Methodology of Qualitative Research: Key Attributes and

    A case study is one of the most commonly used methodologies of social research. This article attempts to look into the various dimensions of a case study research strategy, the different epistemological strands which determine the particular case study type and approach adopted in the field, discusses the factors which can enhance the effectiveness of a case study research, and the debate ...

  24. Additional information about International Student Program reforms

    Ottawa, February 5, 2024—Further information is being provided to clarify the announcement of an intake cap on new international study permit applications and other changes.International students make important contributions to Canada's campuses, communities and economy; however, we have seen unsustainable growth in the International Student Program in recent years.