Abstract
In longitudinal clinical studies, after randomization at baseline, subjects are followed for a period of time for development of symptoms. A mixed model for repeated measures (MMRM) can be used to analyze such data. To accommodate safety and tolerability, in some studies, the treatment has to be titrated to the optimal dose. Then subjects will stay on their optimal dose until the end of the study. In an MMRM analysis, one may attempt to ignore the titration visits because including them could add extra variability unnecessarily. However, when patients drop out during the titration period, ignoring the titration visits would exclude these patients from the analyses. In this article, we evaluate the impact of excluding and including titration visits in an MMRM analysis by a simulation study. We evaluate the approaches based on the bias and the coverage accuracy of the confidence interval. The results suggest that excluding titration visits may result in undercovered confidence intervals and biased estimates.
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