Bayesian Approach for Missing Data Analysis – a Case Study
*Frank Liu, Merck & Co. Inc. 


Missing data is common in clinical trials and could lead to biased estimation of treatment effects. The National Research Council (NRC) report suggests that sensitivity analysis on missing data mechanism should be mandatory component of the primary reporting of findings from clinical trials, and regulatory agencies are requesting more thorough sensitivity analyses from sponsors. However, recent literature research showed that missing data were almost always inadequately handled (Sterne et al, 2009). This is partially due to the lack of standard software packages and straightforward implementation platform.

With recent availability of flexible general purpose Bayesian software packages such as WinBUGS, Proc MCMC, and Stan, it is relatively simple to develop Bayesian methods to address complex missing data problems while incorporating the uncertainty. In this talk, we present some work in progress from DIA Bayesian Scientific Working Group (BSWG) on Bayesian approaches for missing data analysis. We will demonstrate how missing data model can be easily set up and analyzed using Bayesian methods through a case study with a schizophrenia clinical trial.