Recommendations for the Primary Analysis of Continuous Endpoints in Longitudinal Clinical Trials

Abstract

This position paper summarizes relevant theory and current practice regarding the analysis of longitudinal clinical trials intended to support regulatory approval of medicinal products, and it reviews published research regarding methods for handling missing data. It is one strand of the PhRMA initiative to improve efficiency of late-stage clinical research and gives recommendations from a cross-industry team. We concentrate specifically on continuous response measures analyzed using a linear model, when the goal is to estimate and test treatment differences at a given time point. Traditionally, the primary analysis of such trials handled missing data by simple imputation using the last, or baseline, observation carried forward method (LOCF, BOCF) followed by analysis of (co)variance at the chosen time point. However, the general statistical and scientific community has moved away from these simple methods in favor of joint analysis of data from all time points based on a multivariate model (eg, of a mixed-effects type). One such newer method, a likelihood-based mixed-effects model repeated measures (MMRM) approach, has received considerable attention in the clinical trials literature. We discuss specific concerns raised by regulatory agencies with regard to MMRM and review published evidence comparing LOCF and MMRM in terms of validity, bias, power, and type I error. Our main conclusion is that the mixed model approach is more efficient and reliable as a method of primary analysis, and should be preferred to the inherently biased and statistically invalid simple imputation approaches. We also summarize other methods of handling missing data that are useful as sensitivity analyses for assessing the potential effect of data missing not at random.