{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T19:52:27Z","timestamp":1767815547944,"version":"3.49.0"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"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":"\n Background<\/jats:title>\n In recurrent event analysis with fixed follow-up intervals, truncated follow-up due to early dropout or study termination introduces bias and reduces precision in risk estimates, particularly in clinical trials where shorter observation periods may underestimate event risks.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We propose a time-based weighting approach using the ratio of observed-to-expected follow-up duration in the Prentice-Williams-Peterson Gap Time (PWP-GT) model. The method was evaluated in simulations and applied to a double-blinded trial (N\u2009=\u20094000) comparing 500\u2009mg vs. 1500\u2009mg daily calcium supplementation for preeclampsia prevention. For demonstration of the problem and application of the weighting method, drug non-adherence at follow-up visits was considered as the recurrent event.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Simulations showed the weighted PWP-GT model had lower bias (1.0% vs. 1.3%) and improved precision compared to the unweighted model, with coverage probabilities >94%. In the trial data, weighting yielded smaller standard errors and a more conservative hazard ratio for hypertension family history (weighted HR\u2009=\u20091.14, SE\u2009=\u20090.054 vs. unweighted HR\u2009=\u20091.23, SE\u2009=\u20090.065).<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Unaccounted truncated follow-up in recurrent event studies can bias the risk estimation if unaccounted for. Our findings demonstrate that total time-based weighting effectively addresses this bias and enhances precision in both simulated and real datasets.<\/jats:p>\n <\/jats:sec>","DOI":"10.1371\/journal.pone.0339887","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T18:42:10Z","timestamp":1767811330000},"page":"e0339887","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adjusting for truncated study duration in recurrent event analysis: A weighting approach for clinical trials"],"prefix":"10.1371","volume":"21","author":[{"given":"John","family":"Michael Raj A","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1786-6076","authenticated-orcid":true,"given":"Tinku","family":"Thomas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9843-1057","authenticated-orcid":true,"given":"Pratibha","family":"Dwarkanath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"issue":"1","key":"pone.0339887.ref001","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1093\/ije\/dyu222","article-title":"Modelling recurrent events: a tutorial for analysis in epidemiology","volume":"44","author":"LD Amorim","year":"2015","journal-title":"Int J Epidemiol"},{"issue":"1","key":"pone.0339887.ref002","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s12874-017-0462-x","article-title":"A systematic comparison of recurrent event models for application to composite endpoints","volume":"18","author":"A-K Ozga","year":"2018","journal-title":"BMC Med Res Methodol"},{"issue":"04","key":"pone.0339887.ref003","doi-asserted-by":"crossref","DOI":"10.4172\/2327-4972.1000354","article-title":"An overview of statistical models for recurrent events analysis: a review","volume":"8","author":"CP Yadav","year":"2018","journal-title":"Epidemiol Open Access"},{"issue":"17","key":"pone.0339887.ref004","doi-asserted-by":"crossref","DOI":"10.1136\/bjsports-2011-090803","article-title":"Statistical modelling for recurrent events: an application to sports injuries","volume":"48","author":"S Ullah","year":"2014","journal-title":"Br J Sports Med"},{"issue":"4","key":"pone.0339887.ref005","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1111\/biom.13229","article-title":"Semiparametric modelling and estimation of covariate-adjusted dependence between bivariate recurrent events","volume":"76","author":"J Ning","year":"2020","journal-title":"Biometrics"},{"issue":"468","key":"pone.0339887.ref006","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1198\/016214504000001033","article-title":"Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data","volume":"99","author":"C-Y Huang","year":"2004","journal-title":"J Am Stat Assoc"},{"key":"pone.0339887.ref007","doi-asserted-by":"crossref","unstructured":"Prentice RL. 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