A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts.

Intention-to-treat (ITT) analysis is commonly used in randomized clinical trials. However, the use of ITT analysis presents a challenge: how to deal with subjects who drop out. Here we focus on randomized trials where the primary outcome is a binary endpoint. Several approaches are available for including the dropout subject in the ITT analysis, mainly chosen prior to unblinding the study. These approaches reduce the potential bias due to breaking the randomization code. However, the validity of the results will highly depend on untestable assumptions about the dropout mechanism. Thus, it is important to evaluate the sensitivity of the results across different missing-data mechanisms. We propose here a Bayesian pattern-mixture model for ITT analysis of binary outcomes with dropouts that applies over different types of missing-data mechanisms. We introduce a new parameterization to identify the model, which is then used for sensitivity analysis. The parameterization is defined as the odds ratio of having an endpoint between the subjects who dropped out and those who completed the study. Such parameterization is intuitive and easy to use in sensitivity analysis; it also incorporates most of the available methods as special cases. The model is applied to TRial Of Preventing HYpertension.