Missing Data in Noninferiority Trials

Abstract

Missing data in clinical trials has been widely discussed in the literature, but issues specific to missing data in noninferiority trials have rarely been addressed. The goal of this article is to present missing data issues that are particularly important in noninferiority trials. Issues of assay sensitivity and the constancy assumption are affected by missing data. Importantly, these issues are not solved by per protocol analyses which remove patient data based on postrandomization criteria. We advocate collecting data to the extent possible for sensitivity analyses. We discuss some other issues that remain unresolved in assessing the impact of missing data in noninferiority trials. A simulation analysis of different strategies for assessing noninferiority in the presence of missing data is reported for a clinical trial comparing two treatments. Single imputation procedures and observed case analyses resulted in reduced power due to missing data and occasionally in inflation in Type I error rate or bias in estimates of treatment effect. The mixed-effect model repeated measures approach resulted in a method that controlled the Type I error rate when data are missing at random, and often with higher power than the other two methods. Further work on multiple imputation procedures is desired.