First speaker: Nate Bean
Title: Bayesian Design of Multi-Regional Clinical Trials with Time-to-Event Endpoints
Abstract: Sponsors often rely on multi-regional clinical trials (MRCTs) to introduce new treatments more rapidly into the global market. Many commonly used statistical methods do not account for regional differences, and small regional sample sizes frequently result in lower estimation quality of region-specific treatment effects. The International Council for Harmonisation E17 guidelines suggest consideration of methods that allow for information borrowing across regions to improve estimation. In response to these guidelines, we develop a novel methodology to estimate global and region-specific treatment effects from MRCTs with time-to-event endpoints using Bayesian model averaging (BMA). This approach accounts for the possibility of heterogeneous treatment effects between regions, and we discuss how to assess the consistency of these effects using posterior model probabilities. We obtain posterior samples of the treatment effects using a Laplace approximation, and we show through simulation studies that the proposed modeling approach estimates region-specific treatment effects with lower MSE than a Cox proportional hazards model while resulting in a similar rejection rate of the global treatment effect. We then apply the BMA approach to data from the LEADER trial, an MRCT designed to evaluate the cardiovascular safety of an anti-diabetic treatment.
Second speaker: Yueqi Shen
Title: Optimal Priors for the Power Parameter of the Normalized Power Prior
Abstract: The power prior has emerged as a popular class of informative priors for incorporating information from historical data in a variety of applications. When the power parameter is modeled as random, the normalized power prior (NPP) is recommended. The NPP defines a conditional prior for the parameters of interest given the power parameter and a marginal prior for the power parameter. In this work, we explore the optimal priors for the power parameter in an NPP. In particular, we are interested in achieving the dual objectives of encouraging borrowing when historical and current data are compatible and limiting borrowing when they are in conflict. We propose an intuitive procedure for eliciting the shape parameters of the beta prior for the power parameter based on Kullback-Leibler divergence. In addition, we prove that the marginal posterior for the power parameter for generalized linear models converges to a point mass at zero for if there is any discrepancy between historical and current data, and that it does not converge to a point mass at one when they are fully compatible.