Handling of baseline measurements in the analysis of crossover trials

Different analytic approaches for modeling baseline data in crossover trials were compared based on the efficiency in estimating treatment effects. Jointly modeling baseline and post-baseline data is recommended to best utilize baseline data. It results in the most significant gain in efficiency when data are strongly correlated within the same period but weakly correlated between different periods. Its performance remains comparable to the best of various other modeling methods under small within period correlation or large between period correlation. We also examined the use of baseline data in modeling carryover effect. We noted that to model carryover effect in crossover trial generally would lead to a much less efficient estimator and much more sensitive inference.