Adjusting for Baseline on the Analysis of Repeated Binary Responses With Missing Data

Abstract

Little research has been done to evaluate the effect of adjusting for baseline in the analysis of repeated incomplete binary data through simulation study. In this article, covariate adjusted and unadjusted implementations of the following methods were compared in analyzing incomplete repeated binary data when the outcome at the study endpoint is of interest: logistic regression with the last observation carried forward (LOCF), generalized estimating equations (GEE), weighted GEE (WGEE), generalized linear mixed model (GLMM), and multiple imputation (MI) with analyses via GEE. Incomplete data mimicking several clinical trial scenarios were generated using missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) mechanisms. Across the various analytic methods and scenarios covariate adjusted analyses generally yielded larger, less biased treatment effect estimates and larger standard errors compared with their unadjusted counterpart. The net result of these factors was increased power from the covariate adjusted analyses without increasing Type I error rates. Although all methods were biased in at least some of the MNAR scenarios, the Type I error rates from LOCF exceeded 20% whereas the highest rate from any other method in any scenario was less than 10%. LOCF also yielded biased results in MCAR and MAR data whereas the other methods were not biased or had smaller biases than LOCF. These results support longitudinal modeling of repeated binary data over LOCF logistic regression of the study endpoint only. These results also support covariate adjustment for baseline severity in these longitudinal models.