Bayesian Prediction of Event Times Using Mixture Model for Blinded Randomized Controlled Trials.
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| Titel: | Bayesian Prediction of Event Times Using Mixture Model for Blinded Randomized Controlled Trials. |
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| Autoren: | Fu J; Department of Statistics, Rice University, Houston, Texas, USA., Zhao D; Global Biometrics, Servier Pharmaceuticals, Boston, Massachusetts, USA., Skanji D; Institut de Recherches Internationales Servier (IRIS), Gif-sur-Yvette, France., Liu H; Global Biometrics, Servier Pharmaceuticals, Boston, Massachusetts, USA., Tang RS; Quantitative Sciences and Evidence Generation, Astellas Pharmaceuticals, Boston, Massachusetts, USA., Yuan Y; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. |
| Quelle: | Statistics in medicine [Stat Med] 2025 Dec; Vol. 44 (28-30), pp. e70310. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Chichester ; New York : Wiley, c1982- |
| MeSH-Schlagworte: | Randomized Controlled Trials as Topic*/methods , Randomized Controlled Trials as Topic*/statistics & numerical data , Models, Statistical*, Bayes Theorem ; Humans ; Computer Simulation ; Time Factors ; Clinical Trials, Phase III as Topic/methods ; Clinical Trials, Phase III as Topic/statistics & numerical data |
| Abstract: | Accurate prediction of key milestone dates, such as the timing of interim and final analyses, is crucial in event-driven clinical trials with time-to-event endpoints. These predictions facilitate timely decision-making, enhance strategic planning and optimize resource allocation while minimizing patient exposure to potentially ineffective or harmful therapies. Existing methods for predicting event timing in blinded randomized clinical trials (RCTs) typically assume identical time-to-event distributions for the treatment and control arms, implying no treatment effect. This assumption fails when the treatment is more effective than the control, which is often the very outcome the trial seeks to detect, leading to biased predictions. To address this issue, we propose a novel Bayesian Prediction of Event Times (BayesPET) method that allows for different time-to-event distributions between arms in blinded RCTs. We employ a mixture Weibull model for the observed interim event times, while addressing the critical challenge of label-switching in mixture models through truncated priors. Through extensive simulations and real-world applications to phase 3 clinical trials, we demonstrate the BayesPET produces superior predictive performance in both blinded and unblinded settings, supporting effective trial execution and accelerating the development of new therapies. (© 2025 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.) |
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| Contributed Indexing: | Keywords: Bayesian model; event prediction; label‐switching; mixture‐Weibull model; time‐to‐event endpoints |
| Entry Date(s): | Date Created: 20251125 Date Completed: 20251125 Latest Revision: 20251129 |
| Update Code: | 20251129 |
| PubMed Central ID: | PMC12646817 |
| DOI: | 10.1002/sim.70310 |
| PMID: | 41290204 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Accurate prediction of key milestone dates, such as the timing of interim and final analyses, is crucial in event-driven clinical trials with time-to-event endpoints. These predictions facilitate timely decision-making, enhance strategic planning and optimize resource allocation while minimizing patient exposure to potentially ineffective or harmful therapies. Existing methods for predicting event timing in blinded randomized clinical trials (RCTs) typically assume identical time-to-event distributions for the treatment and control arms, implying no treatment effect. This assumption fails when the treatment is more effective than the control, which is often the very outcome the trial seeks to detect, leading to biased predictions. To address this issue, we propose a novel Bayesian Prediction of Event Times (BayesPET) method that allows for different time-to-event distributions between arms in blinded RCTs. We employ a mixture Weibull model for the observed interim event times, while addressing the critical challenge of label-switching in mixture models through truncated priors. Through extensive simulations and real-world applications to phase 3 clinical trials, we demonstrate the BayesPET produces superior predictive performance in both blinded and unblinded settings, supporting effective trial execution and accelerating the development of new therapies.<br /> (© 2025 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.) |
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| ISSN: | 1097-0258 |
| DOI: | 10.1002/sim.70310 |