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

• Published in: Pharmaceutical statistics : the journal of the pharmaceutical industry Vol. 21; no. 2; pp. 327 - 344
• Main Authors: Lin, Junjing, Gamalo‐Siebers, Margaret, Tiwari, Ram
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.03.2022; Wiley Subscription Services, Inc
• Subjects: Bayes Theorem; Bayesian analysis; Bayesian augmented control; Child; Clinical trials; Computer Simulation; Disease; exchangeability; Hellinger distance; historical control; Humans; propensity score; real‐world data; Research Design; Sample Size; Selection Bias; Hellinger distance; exchangeability; real-world data; historical control; Bayesian augmented control; propensity score
• ISSN: 1539-1604; 1539-1612; 1539-1612
• DOI: 10.1002/pst.2172

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.