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In many clinical trials, outcomes of interest include binary-valuedendpoints. It is not uncommon that a binary-valued outcome is dichotomized froma continuous outcome at a threshold of clinical interest. To reach theobjective, common approaches include (a) fitting the generalized linear mixedmodel (GLMM) to the dichotomized longitudinal binary outcome and (b) imputationmethod (MI): imputing the missing values in the continuous outcome,dichotomizing it into a binary outcome, and then fitting the generalized linearmodel for the "complete" data. We conducted comprehensive simulation studies tocompare the performance of GLMM with MI for estimating risk difference andlogarithm of odds ratio between two treatment arms at the end of study. Inthose simulation studies, we considered a range of multivariate distributionoptions for the continuous outcome (including a multivariate normaldistribution, a multivariate t-distribution, a multivariate log-normaldistribution, 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 thoseconsidered distribution options, but MI is more efficient with smaller meansquared errors compared to GLMM. We further applied both the GLMM and MI to 29phase 3 diabetes clinical trials, and found that the MI method generally led tosmaller variance estimates compared to GLMM.

Analysis of an Incomplete Binary Outcome Dichotomized From an Underlying Continuous Variable in Clinical Trials