Enhancing site selection strategies in clinical trial recruitment using real-world data modeling

Lars Hulstaert; Isabell Twick; Khaled Sarsour; Hans Verstraete

doi:10.1371/journal.pone.0300109

Peer Review History

Original SubmissionNovember 22, 2023
18 Jan 2024 Decision Letter - Krit Pongpirul, Editor PONE-D-23-38747Enhancing site selection strategies in clinical trial recruitment using real-world data modelingPLOS ONE Dear Dr. Hulstaert, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Mar 03 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Additional Editor Comments: Please address the comments raised by both reviewers. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: -- Summary -- The authors present a ML-based model for ranking clinical trial sites by their expected recruitment potential for clinical trial site selection. They employ historical recruitment data combined with real-world claims data and additional public data sources for model training and evaluation. Models are trained, evaluated and modeling approaches compared for two diseases indications. -- Comments -- - Definition of patient cohorts for benchmark studies: Suppl. file S2 indicates that multiple clinical studies were used as reference for the identified patient cohorts. Please comment on how the cohort inclusion criteria for the presented 'experiments' were derived from the inclusion criteria of these clinical studies. Presumably, inclusion criteria were not identical for all clinical studies, how was consensus reached for the experiments? - Mismatch between RWD cohort observation period and recruitment data: "As such, the cohort observation period is used instead to characterize the real-world clinical practice of a site, and it is assumed to remain constant over time". Please discuss the potential biases that may be introduced by estimating site level covariates for the recruitment into studies performed much earlier (about 15-20 years, 1990 onwards) from rather recent RWD (2016 onwards). - Manual covariate selection: What thresholds have been applied and which covariates were selected/removed? Does fig 4 show only the selected covariates? - Authors state in the discussion that 'The non-linear model performance significantly improves the ability to rank the sites by expected enrollment ...', however, the methods section indicates that statistical significance testing has been performed on the MAE which is not a direct measure of ranking performance. Please explain. - Fig 1-3: Please indicate the unit of 'frequency' in figs 1-3. 'Studies per year' in fig 1? But 'number of studies' in fig 2 and fig 3? - Table 4: The reported results are labeled as 'Test'. Presumably this refers to the 20% of the 80/20 split mentioned in line 217. Please clarify the nomenclature. It would be interesting to see how robust these performance estimates are for different sample splits. - Table S6 does not contain train model performances as mentioned in line 265. Reviewer #2: This is a comprehensive machine learning study for site selection using trial data and real world data. A linear Poisson regression model and a non-linear XGBoost model were trained and compared with the baseline method. It clearly shows that XGBoost outperforms other methods. Shapley values were used to estimate covariate importance in the model. It helps interpret the relationship of study recruitment with the different covariates. Interestingly it highlights that different factors play a role in recruitment for different indications such as IBD and MM. Together these methods demonstrate the value of machine learning models in improving site selections. Since there are many non-linear models besides XGBoost, it would be interesting to add a few more models to test how different algorithms perform in the two indications. Since the training/test data are ready, this seems straightforward. But if there are practical reasons why other algorithms were not selected, you may simply add the explanation. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: Yes: Xiong Liu ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0300109.r001
Revision 1
29 Jan 2024 Author Response Addressed reviewer comments in 'Response to Reviewers' letter. Please let us know if additional changes or revisions are required. Reviewer 1 Comments Definition of patient cohorts for benchmark studies: Suppl. file S2 indicates that multiple clinical studies were used as reference for the identified patient cohorts. Please comment on how the cohort inclusion criteria for the presented 'experiments' were derived from the inclusion criteria of these clinical studies. Presumably, inclusion criteria were not identical for all clinical studies, how was consensus reached for the experiments?  Updated line 166: Inclusion criteria of benchmark studies are used to define a superset of relevant diagnosis, drugs, and procedures codes. These codes define a patient cohort that represents the broad patient population that is eligible for the benchmark studies. Mismatch between RWD cohort observation period and recruitment data: "As such, the cohort observation period is used instead to characterize the real-world clinical practice of a site, and it is assumed to remain constant over time". Please discuss the potential biases that may be introduced by estimating site level covariates for the recruitment into studies performed much earlier (about 15-20 years, 1990 onwards) from rather recent RWD (2016 onwards). Updated line line 212: The variability in yearly calculations of the site level RWD covariates across the available data is sufficiently small, allowing them to be approximated as constant when averaged across the cohort observation period. Before 2016 it is not possible to validate this hypothesis which has the potential to introduce data bias in RWD covariates for studies conducted before 2016. Manual covariate selection: What thresholds have been applied and which covariates were selected/removed? Does fig 4 show only the selected covariates? Updated line 269: Covariates with a variable importance, as defined by the covariate mean SHAP value, that is below 0.005 are removed from the covariate set. For each model, the selected set of covariates is defined in S4 File, which is a subset of the full set of covariates described in Table 3. Fig 4 description is adapted to “Covariate importance of the selected covariates for the XGBoost models” Authors state in the discussion that 'The non-linear model performance significantly improves the ability to rank the sites by expected enrollment ...', however, the methods section indicates that statistical significance testing has been performed on the MAE which is not a direct measure of ranking performance. Please explain. Updated line 306 and 320 to reflect the type of statistical testing that was applied. Fig 1-3: Please indicate the unit of 'frequency' in figs 1-3. 'Studies per year' in fig 1? But 'number of studies' in fig 2 and fig 3?   Descriptions of Fig 1-3 are adapted (line 138 to 145): Fig 1. Overview of number of benchmark studies across phase and indication. An overview of the number of benchmark studies across study phase and study indication for resp. IBD and MM. Fig 2. Overview of number of patients enrolled and number of enrollment months across study-site combinations. An overview of the number of patients enrolled and number of enrollment months per site across the benchmark studies for resp. IBD and MM. Fig 3. Overview of number of sites across US states and open year. An overview of number of sites across US states and site open year for resp. IBD and MM. Table 4: The reported results are labeled as 'Test'. Presumably this refers to the 20% of the 80/20 split mentioned in line 217. Please clarify the nomenclature. It would be interesting to see how robust these performance estimates are for different sample splits. Updated line 224: We use a random train (80%) and test (20%) data split at site level to avoid the potential of a data distribution bias and corresponding impact on model generalization capabilities. Table S6 does not contain train model performances as mentioned in line 265. Updated Table S6 to contain the train model performance. Reviewer 2 Comments Since there are many non-linear models besides XGBoost, it would be interesting to add a few more models to test how different algorithms perform in the two indications. Since the training/test data are ready, this seems straightforward. But if there are practical reasons why other algorithms were not selected, you may simply add the explanation. Updated Table 4 with results for RandomForest experiments. Updated line 253: For each exercise, a linear Poisson regression model and two non-linear machine learning models, a RandomForest and an XGBoost model (v.1.7.2), are trained and compared with the median and site baseline. We considered other non-linear models but didn’t observe a significant difference in performance. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0300109.r002
23 Feb 2024 Decision Letter - Krit Pongpirul, Editor Enhancing site selection strategies in clinical trial recruitment using real-world data modeling PONE-D-23-38747R1 Dear Dr. Hulstaert, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Thank you for addressing the comments. Please consider the following questions and recommendations below optional: - You mention in your answers (update line 212) that "The variability in yearly calculations of the site level RWD covariates across the available data is sufficiently small, allowing them to be approximated as constant when averaged across the cohort observation period. Before 2016 it is not possible to validate this hypothesis which has the potential to introduce data bias in RWD covariates for studies conducted before 2016." -> Readers may be interested in how this hypothesis been verified for the period from 2016. - Thank you for clarifying the meaning of 'frequency' in the captions of Figs 1-3. -> Modifying the figures' y-axis accordingly would greatly facilitate the reading in my opinion. Reviewer #2: The authors have adequately addressed my comments. The paper is now in a good shape for publication. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: Yes: Xiong Liu ******** https://doi.org/10.1371/journal.pone.0300109.r003
Formally Accepted
29 Feb 2024 Acceptance Letter - Krit Pongpirul, Editor PONE-D-23-38747R1 PLOS ONE Dear Dr. Hulstaert, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Assoc. Prof. Dr. Krit Pongpirul Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0300109.r004

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