Analysis of an incomplete binary outcome derived from frequently recorded longitudinal continuous data: application to daily pain evaluation.

Randomized clinical trials increasingly collect daily data, frequently using electronic diaries. Such data are usually summarized into an 'intermediate' continuous outcome (such as the mean of the daily values in a period before a scheduled clinic visit). These are in turn often summarized further into a binary outcome, for example, indicating whether the intermediate continuous outcome has improved by a prespecified amount from randomization. This article compares and contrasts statistical approaches for analyzing such binary outcomes when the underlying study is subject to dropout so that some of the underlying diary data are missing. Such analysis involves rigorous rules for the derivation of outcomes, a thorough data exploration for the selection of covariates, and an elucidation of the missingness mechanism. The investigated statistical methods for treatment-effect analysis are based on direct modeling and on multiple imputation and are applied either to the binary outcome or the intermediate continuous outcome or to the daily diary data. These are compared on the basis of criteria for inferences at prespecified times during the follow-up. We show that multiple-imputation methods are particularly well adapted to our context and that missing data imputation on the daily diary data, rather than the derived outcomes, makes best use of the available information. The data set, which motivated our investigation, comes from a placebo-controlled clinical trial to assess the effect on pain of a new compound.