A Comparison of Frequentist and Bayesian Model Based Approaches for Missing Data Analysis: Case Study with a Schizophrenia Clinical Trial

The National Research Council (NRC) report suggests that sensitivity analysis on missing data mechanism should be a 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 Bayesian software packages such as WinBUGS, SAS Proc MCMC, and Stan, it is relatively simple to develop Bayesian methods to address complex missing data problems while incorporating the uncertainty. In this paper, we present a case study from the DIA Bayesian Scientific Working Group (BSWG) on Bayesian approaches for missing data analysis.