10/13/2017

Post date: Nov 1, 2017 7:38:47 PM

Title: Control-based Imputation for Missing Data Handling in Longitudinal Clinical Trials

Speaker: G. Frank Liu, Ph.D., Merck Research Laboratories, North Wales, PA

[Abstract] Control-based imputation, an approach which imputes missing data in the test drug group using a model built from the control group, has gained more attention in recent research for handling missing data in clinical trials. This control-based imputation (CBI) approach typically provides a conservative point estimate for treatment difference, and addresses an estimand which has some causal-effect interpretation. A standard multiple imputation approach with Rubin’s rule is commonly used to implement this method. However, the combined variance from Rubin’s rule may over-estimate the variance, therefore reduce power for treatment comparison in statistical inference. In this talk, we discuss some alternative methods on getting more appropriate variance for CBI analysis in different types of outcomes from longitudinal clinical trials including continuous, binary, and recurrent time to event. Applications to several real clinical trial datasets are presented for illustration.