On analysis of longitudinal clinical trials with missing data using reference-based imputation

<p>Reference-based imputation (RBI) methods have been proposed as sensitivity analyses for longitudinal clinical trials with missing data. The RBI methods multiply impute the missing data in treatment group based on an imputation model built using data from the reference (control) group. The RBI will yield a conservative treatment effect estimate as compared to the estimate obtained from multiple imputation (MI) under missing at random (MAR). However, the RBI analysis based on the regular MI approach can be overly conservative because it not only applies discount to treatment effect estimate but also posts penalty on the variance estimate. In this article, we investigate the statistical properties of RBI methods, and propose approaches to derive accurate variance estimates using both frequentist and Bayesian methods for the RBI analysis. Results from simulation studies and applications to longitudinal clinical trial datasets are presented.</p

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

<p>Missing data are 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 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. 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 article, we present a case study from the DIA Bayesian Scientific Working Group (BSWG) on Bayesian approaches for missing data analysis. We illustrate how to use Bayesian approaches to fit a few commonly used frequentist missing data models. The properties, advantage, and flexibility of the Bayesian analysis methods will be discussed using a case study based on a schizophrenia clinical trial. Supplementary materials for this article are available online.</p