{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T04:41:22Z","timestamp":1789274482006,"version":"build-2803163510"},"reference-count":26,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T00:00:00Z","timestamp":1710115200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Slow patient enrollment or failing to enroll the required number of patients is a disruptor of clinical trial timelines. To meet the planned trial recruitment, site selection strategies are used during clinical trial planning to identify research sites that are most likely to recruit a sufficiently high number of subjects within trial timelines. We developed a machine learning approach that outperforms baseline methods to rank research sites based on their expected recruitment in future studies. Indication level historical recruitment and real-world data are used in the machine learning approach to predict patient enrollment at site level. We define covariates based on published recruitment hypotheses and examine the effect of these covariates in predicting patient enrollment. We compare model performance of a linear and a non-linear machine learning model with common industry baselines that are constructed from historical recruitment data. Performance of the methodology is evaluated and reported for two disease indications, inflammatory bowel disease and multiple myeloma, both of which are actively being pursued in clinical development. We validate recruitment hypotheses by reviewing the covariates relationship with patient recruitment. For both indications, the non-linear model significantly outperforms the baselines and the linear model on the test set. In this paper, we present a machine learning approach to site selection that incorporates site-level recruitment and real-world patient data. The model ranks research sites by predicting the number of recruited patients and our results suggest that the model can improve site ranking compared to common industry baselines.<\/jats:p>","DOI":"10.1371\/journal.pone.0300109","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T13:28:51Z","timestamp":1710163731000},"page":"e0300109","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":16,"title":["Enhancing site selection strategies in clinical trial recruitment using real-world data modeling"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8667-7881","authenticated-orcid":true,"given":"Lars","family":"Hulstaert","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabell","family":"Twick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khaled","family":"Sarsour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans","family":"Verstraete","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,3,11]]},"reference":[{"key":"pone.0300109.ref001","doi-asserted-by":"crossref","first-page":"51","DOI":"10.4103\/picr.PICR_6_20","article-title":"Recruitment and retention of participants in clinical studies: Critical issues and challenges.","volume":"11","author":"M. Desai","year":"2020","journal-title":"Perspect Clin Res"},{"key":"pone.0300109.ref002","doi-asserted-by":"crossref","first-page":"2511","DOI":"10.1161\/STROKEAHA.117.016976","article-title":"Factors Associated With Time to Site Activation, Randomization, and Enrollment Performance in a Stroke Prevention Trial","volume":"48","author":"BM Demaerschalk","year":"2017","journal-title":"Stroke"},{"key":"pone.0300109.ref003","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.conctc.2018.08.001","article-title":"Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review.","volume":"11","author":"DB Fogel","year":"2018","journal-title":"Contemp Clin Trials Commun"},{"key":"pone.0300109.ref004","doi-asserted-by":"crossref","first-page":"e014796","DOI":"10.1136\/bmjopen-2016-014796","article-title":"Improving site selection in clinical studies: a standardised, objective, multistep method and first experience results","volume":"7","author":"A Hurtado-Chong","year":"2017","journal-title":"BMJ Open"},{"key":"pone.0300109.ref005","doi-asserted-by":"crossref","first-page":"400","DOI":"10.3109\/00952990.2011.596975","article-title":"Site selection in community-based clinical trials for substance use disorders: Strategies for effective site selection","volume":"37","author":"JS Potter","year":"2011","journal-title":"Am J Drug Alcohol Abuse"},{"key":"pone.0300109.ref006","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1186\/s13063-019-3790-9","article-title":"Criteria for site selection in industry-sponsored clinical trials: a survey among decision-makers in biopharmaceutical companies and clinical research organizations","volume":"20","author":"T Dombernowsky","year":"2019","journal-title":"Trials"},{"key":"pone.0300109.ref007","author":"The Role of Retail Pharmacies in the Evolving Landscape of Clinical Research","year":"2023","journal-title":"Appl Clin Trials Online"},{"key":"pone.0300109.ref008","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1177\/17407745211038512","article-title":"Clinical trial site identification practices and the use of electronic health records in feasibility evaluations: An interview study in the Nordic countries.","volume":"18","author":"N Laaksonen","year":"2021","journal-title":"Clin Trials"},{"key":"pone.0300109.ref009","author":"E. Miseta","journal-title":"Bring Down The Cost Of Clinical Trials With Improved Site Selection n.d."},{"key":"pone.0300109.ref010","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.ijmedinf.2017.10.003","article-title":"Systematic data ingratiation of clinical trial recruitment locations for geographic-based query and visualization","volume":"108","author":"J Luo","year":"2017","journal-title":"Int J Med Inf"},{"key":"pone.0300109.ref011","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/1471-2288-10-63","article-title":"A systematic review of models to predict recruitment to multicentre clinical trials.","volume":"10","author":"KD Barnard","year":"2010","journal-title":"BMC Med Res Methodol"},{"key":"pone.0300109.ref012","doi-asserted-by":"crossref","first-page":"5458","DOI":"10.1002\/sim.5979","article-title":"Some issues in predicting patient recruitment in multi-centre clinical trials.","volume":"32","author":"A Bakhshi","year":"2013","journal-title":"Stat Med"},{"key":"pone.0300109.ref013","author":"Getz","journal-title":"Predicting Successful Site Performance n.d"},{"key":"pone.0300109.ref014","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10741-012-9361-8","article-title":"Site selection in global clinical trials in patients hospitalized for heart failure: perceived problems and potential solutions","volume":"19","author":"M Gheorghiade","year":"2014","journal-title":"Heart Fail Rev"},{"key":"pone.0300109.ref015","doi-asserted-by":"crossref","first-page":"e002957","DOI":"10.1136\/bmjopen-2013-002957","article-title":"Factors influencing clinical trial site selection in Europe: The Survey of Attitudes towards Trial sites in Europe (the SAT-EU Study).","volume":"3","author":"M Gehring","year":"2013","journal-title":"BMJ Open"},{"key":"pone.0300109.ref016","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.cct.2018.01.003","article-title":"Clinical trials recruitment planning: A proposed framework from the Clinical Trials Transformation Initiative.","volume":"66","author":"GD Huang","year":"2018","journal-title":"Contemp Clin Trials"},{"key":"pone.0300109.ref017","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.conctc.2017.07.004","article-title":"Predicting enrollment performance of investigational centers in phase III multi-center clinical trials.","volume":"7","author":"RM van den Bor","year":"2017","journal-title":"Contemp Clin Trials Commun"},{"key":"pone.0300109.ref018","doi-asserted-by":"crossref","first-page":"e0263193","DOI":"10.1371\/journal.pone.0263193","article-title":"Prediction of clinical trial enrollment rates.","volume":"17","author":"C Bieganek","year":"2022","journal-title":"PloS One"},{"key":"pone.0300109.ref019","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1002\/sim.3847","article-title":"Stochastic modeling and prediction for accrual in clinical trials.","volume":"29","author":"X Zhang","year":"2010","journal-title":"Stat Med"},{"key":"pone.0300109.ref020","author":"J Liu","year":"2021","journal-title":"A Machine Learning Approach for Recruitment Prediction in Clinical Trial Design"},{"key":"pone.0300109.ref021","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1093\/jamia\/ocz064","article-title":"Optimizing clinical trials recruitment via deep learning.","volume":"26","author":"J Gligorijevic","year":"2019","journal-title":"J Am Med Inform Assoc JAMIA"},{"key":"pone.0300109.ref022","unstructured":"NPPES n.d. https:\/\/nppes.cms.hhs.gov\/#\/ (accessed June 19, 2023)."},{"key":"pone.0300109.ref023","author":"T Chen","year":"2016","journal-title":"XGBoost: A Scalable Tree Boosting System"},{"key":"pone.0300109.ref024","author":"R Liaw","year":"2018","journal-title":"Tune: A Research Platform for Distributed Model Selection and Training"},{"key":"pone.0300109.ref025","first-page":"4765","volume-title":"Adv. Neural Inf. Process. Syst.","author":"SM Lundberg","year":"2017"},{"key":"pone.0300109.ref026","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.cpcardiol.2018.11.002","article-title":"Increasing Diversity in Clinical Trials: Overcoming Critical Barriers.","volume":"44","author":"LT Clark","year":"2019","journal-title":"Curr Probl Cardiol"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0300109","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T13:29:10Z","timestamp":1710163750000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0300109"}},"subtitle":[],"editor":[{"given":"Krit","family":"Pongpirul","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,3,11]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3,11]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0300109","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,11]]}}}