Handling of missing data in long-term clinical trials: a case study

Abstract

Missing data in clinical trials is a well-known problem, and the classical statistical methods used can be overly simple. This case study shows how well-established missing data theory can be applied to efficacy data collected in a long-term open-label trial with a discontinuation rate of almost 50%. Satisfaction with treatment in chronically constipated patients was the efficacy measure assessed at baseline and every 3?months postbaseline. The improvement in treatment satisfaction from baseline was originally analyzed with a paired t-test ignoring missing data and discarding the correlation structure of the longitudinal?data. As the original analysis started from missing completely at random assumptions regarding the missing data process, the satisfaction data were re-examined, and several missing at random (MAR) and missing not at random (MNAR)?techniques resulted in adjusted estimate for the improvement in satisfaction over 12?months. Throughout the different sensitivity analyses, the effect sizes remained significant and clinically relevant. Thus, even for an open-label trial design, sensitivity analysis, with different assumptions for the nature of dropouts (MAR or MNAR) and with different classes of models (selection, pattern-mixture, or multiple imputation models), has been found useful and provides evidence towards the robustness of the original analyses; additional sensitivity analyses could be undertaken to further qualify robustness.