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Activity Number: 60
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Consulting
Abstract #318978
Title: Predicting the Timing of the Final Event in a Clinical Trial Using the Bayesian Bootstrap and Beyond
Author(s): Marc Sobel* and Ibrahim Turkoz
Companies: Temple University and Janssen R&D
Keywords: Survival analysis ; Bayesian statistics ; Clinical Trials ; Bayesian bootstrap ; nonparametric Bayesian statistics
Abstract:

Variable length time to event clinical trials are based on observing predetermined total numbers of events. The primary events of interest are e.g., death, relapse, adverse drug reaction, or new disease development. We propose strategies for determining the timing of the final events in blinded settings. Parametric naïve models for determining the timing of final events: (i) ignore issues involving staggered entry; (ii) do not take account of the evolution of hazard rates; and (iii) fail to provide model flexibility. Nonparametric Bayesian survival models and the Bayesian bootstrap address these concerns and are shown to be highly accurate predictors. Methodologies are tested using a randomized, double-blind, placebo-controlled trial Schizoaffective trial where the primary endpoint was time to relapse.


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