Analysis of an incomplete binary outcome dichotomized from an underlying continuous variable in clinical trials. (11th March 2022) - Ma, Chenchen; Shen, Xin; Qu, Yongming; Du, Yu
Abstract: In many clinical trials, outcomes of interest are binary‐valued. It is not uncommon that a binary‐valued outcome is dichotomized from a continuous outcome at a threshold of clinical interest. To analyze such data, common approaches include (a) fitting a generalized linear mixed model (GLMM) to the dichotomized longitudinal binary outcome; and (b) the multiple imputation (MI) based method: imputing missing values in the continuous outcome, dichotomizing it into a binary outcome, and then fitting a generalized linear model to the "complete" data. We conducted comprehensive simulation studies to compare the performance of the GLMM versus the MI‐based method for estimating the risk difference and the logarithm of odds ratio between two treatment arms at the end of study. In those simulation studies, we considered a range of multivariate distribution options for the continuous outcome (including a multivariate normal distribution, a multivariate t‐distribution, a multivariate log‐normal distribution, and the empirical distribution from a real clinical trial data) to evaluate the robustness of the estimators to various data‐generating models. Simulation results demonstrate that both methods work well under those considered distribution options, but the MI‐based method is more efficient with smaller mean squared errors compared to the GLMM. We further applied both the GLMM and the MI‐based method to 29 phase 3 diabetes clinical trials, and found that the MI‐based method generally led to smaller variance estimates compared to the GLMM.