A Bayesian piecewise exponential phase II design for monitoring a time‐to‐event endpoint. (18th July 2022)

• Record Type: Journal Article
• Authors: Qing, Yun; Thall, Peter F.; Yuan, Ying
• Is Part Of: Pharmaceutical statistics. Volume 22:Number 1(2023)
• Page Start: 34
• Page End: 44
• Publication Date: 2022-07-18
• Subjects: Bayesian adaptive design -- futility monitoring -- go/no‐go decision -- interim analysis; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.2256
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract: A robust Bayesian design is presented for a single‐arm phase II trial with an early stopping rule to monitor a time to event endpoint. The assumed model is a piecewise exponential distribution with non‐informative gamma priors on the hazard parameters in subintervals of a fixed follow up interval. As an additional comparator, we also define and evaluate a version of the design based on an assumed Weibull distribution. Except for the assumed models, the piecewise exponential and Weibull model based designs are identical to an established design that assumes an exponential event time distribution with an inverse gamma prior on the mean event time. The three designs are compared by simulation under several log‐logistic and Weibull distributions having different shape parameters, and for different monitoring schedules. The simulations show that, compared to the exponential inverse gamma model based design, the piecewise exponential design has substantially better performance, with much higher probabilities of correctly stopping the trial early, and shorter and less variable trial duration, when the assumed median event time is unacceptably low. Compared to the Weibull model based design, the piecewise exponential design does a much better job of maintaining small incorrect stopping probabilities in cases where the true median survival time is desirably large.