  In many clinical trials, outcomes of interest include binary-valued
endpoints. It is not uncommon that a binary-valued outcome is dichotomized from
a continuous outcome at a threshold of clinical interest. To reach the
objective, common approaches include (a) fitting the generalized linear mixed
model (GLMM) to the dichotomized longitudinal binary outcome and (b) imputation
method (MI): imputing the missing values in the continuous outcome,
dichotomizing it into a binary outcome, and then fitting the generalized linear
model for the "complete" data. We conducted comprehensive simulation studies to
compare the performance of GLMM with MI for estimating risk difference and
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 MI is more efficient with smaller mean
squared errors compared to GLMM. We further applied both the GLMM and MI to 29
phase 3 diabetes clinical trials, and found that the MI method generally led to
smaller variance estimates compared to GLMM.
