Ensuring exchangeability in data‐based priors for a Bayesian analysis of clinical trials

Abstract

In many orphan diseases and pediatric indications, the randomized controlled trials may be infeasible because of their size, duration, and cost. Leveraging information on the control through a prior can potentially reduce sample size. However, unless an objective prior is used to impose complete ignorance for the parameter being estimated, it results in biased estimates and inflated type‐I error. Hence, it is essential to assess both the confirmatory and supplementary knowledge available during the construction of the prior to avoid “cherry‐picking” advantageous information. For this purpose, propensity score methods are employed to minimize selection bias by weighting supplemental control subjects according to their similarity in terms of pretreatment characteristics to the subjects in the current trial. The latter can be operationalized through a proposed measure of overlap in propensity‐score distributions. In this paper, we consider single experimental arm in the current trial and the control arm is completely borrowed from the supplemental data. The simulation experiments show that the proposed method reduces prior and data conflict and improves the precision of the of the average treatment effect.

Adaptively leveraging external data with robust meta‐analytical‐predictive prior using empirical Bayes

“…Ignoring such imbalances may lead to bias and erroneous conclusions. This fact prompts new methods being proposed to take the baseline covariates into consideration when adjusting the extent of borrowing 28–30 . It is of future research interest to explore whether EB‐rMAP prior can be implemented in combination with such methods to yield more robust analyses with RWE.…”