{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T19:35:03Z","timestamp":1773948903997,"version":"3.50.1"},"reference-count":48,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002794","name":"Cancerfonden","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmaceutical Statistics"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Bayesian predictive probabilities of success (PPoS) use interim trial data to calculate the probability of trial success. These quantities can be used to optimise trial size or to stop for futility. In this paper, we describe a simulation\u2010based approach to compute the PPoS for clinical trials with competing event data, for which no specific methodology is currently available. The proposed procedure hinges on modelling the joint distribution of time to event and event type by specifying Bayesian models for the cause\u2010specific hazards of all event types. This allows the prediction of outcome data at the conclusion of the trial. The PPoS is obtained by numerically averaging the probability of success evaluated at fixed parameter values over the posterior distribution of the parameters. Our work is motivated by two randomised clinical trials: the I\u2010SPY COVID phase II trial for the treatment of severe COVID\u201019 (NCT04488081) and the STHLM3 prostate cancer diagnostic trial (ISRCTN84445406), both of which are characterised by competing event data. We present different modelling alternatives for the joint distribution of time to event and event type and show how the choice of the prior distributions can be used to assess the PPoS under different scenarios. The role of the PPoS analyses in the decision\u2010making process for these two trials is also discussed.<\/jats:p>","DOI":"10.1002\/pst.70050","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T17:15:37Z","timestamp":1764090937000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Simulation\u2010Based Bayesian Predictive Probability of Success for Interim Monitoring of Clinical Trials With Competing Event Data: Two Case Studies"],"prefix":"10.1002","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6830-6255","authenticated-orcid":false,"given":"Chiara","family":"Micoli","sequence":"first","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics Karolinska Institutet  Stockholm 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