Assessing and interpreting treatment effects in longitudinal clinical trials with missing data

Treatment effects are often evaluated by comparing change over time in outcome measures; however, valid analyses of longitudinal data can be problematic, particularly if some data are missing. For decades, the last observation carried forward (LOCF) approach has been a common method of handling missing data. Considerable advances in statistical methodology and our ability to implement those methods have been made in recent years. Thus, it is appropriate to reconsider analytic approaches for longitudinal data. This review examines the following from a clinical perspective: I) the characteristics of missing data that influence analytic choices; 2) the attributes of common methods of handling missing data; and 3) the use of the data characteristics and the attributes of the various methods, along with empirical evidence, to develop a robust approach for the analysis and interpretation of data from longitudinal clinical trials. We propose that, in many settings, the primary efficacy analysis should use a repeated measures, likelihood-based, mixed-effects modeling approach, with LOCF used as a secondary, composite measure of efficacy, safety, and tolerability. We illustrate how repeated-measures analyses can be used to enhance decision-making, and we review the caveats that remain regarding the use of LOCF as a composite measure.