TY - JOUR
AU1 - CARLIN, BRADLEY P.
AU2 - SARGENT, DANIEL J.
AB - The interim monitoring and final analysis of data arising from a clinical trial require an inferential method capable of convincing a broad group of potential consumers: doctors; patients; politicians; members of the media, and so on. While Bayesian methods offer a powerful and flexible analytic framework in this setting, this need to convince a diverse community necessitates a practical approach for studying and communicating the robustness of conclusions to the prior specification. In this paper we attempt to characterize the class of priors leading to a given decision (such as stopping the trial and rejecting the null hypothesis) conditional on the observed data. We evaluate the practicality and effectiveness of this procedure over a range of smoothness conditions on the prior class. First, we consider a non‐parametric class of priors restricted only in that its elements must have certain prespecified quantiles. We then obtain more precise results by further restricting the prior class, first to a non‐parametric class whose members are quasi‐unimodal, then to a semi‐parametric normal mixture class, and finally to the fully parametric normal family. We illustrate all of our comparisons with a dataset from an AIDS clinical trial that compared the effectiveness of the drug pyrimethamine and a placebo in preventing toxoplasmic encephalitis.
TI - ROBUST BAYESIAN APPROACHES FOR CLINICAL TRIAL MONITORING
JF - Statistics in Medicine
DO - 10.1002/(SICI)1097-0258(19960615)15:11<1093::AID-SIM231>3.0.CO;2-0
DA - 1996-06-15
UR - https://www.deepdyve.com/lp/wiley/robust-bayesian-approaches-for-clinical-trial-monitoring-o2Do3XTF0C
SP - 1093
EP - 1106
VL - 15
IS - 11
DP - DeepDyve
ER -