Management of Baseline Measurements in Statistical Analysis of 2×2 Crossover Trials - Momenyan, Somaye; Alavi Majd, Hamid (Archives of Advances in Biosciences, 2019)
Introduction: Crossover designs have applications in a wide range of sciences. The simplest and most common of such designs are the two-period, two-treatment (2×2) crossover. As a consequence, each subject provides a 4×1 vector of responses for data analysis in the following chronological order: baseline (period 1), post-baseline (period 1), baseline (period 2), and post-baseline (period 2).Methods: We considered three types of analytic approaches for handling the baselines:1) analysis of variance (ANOVA) method which ignores the first or both period baselines or use a change from baseline analysis 2) analysis of covariance (ANCOVA) method which uses an analysis of covariance where linear functions of one or both baselines are employed as either period-specific or period-invariant covariates 3) Joint modeling method that conducts joint modeling of a linear function of the baseline and post-baseline responses with certain mean constraints for the baseline responses. The crossover clinical trial data was analyzed, using the proposed models. Results: Based on the results on real data among all mentioned models, the first model (direct comparison of post-treatment values) and the second model (post-treatment measurement subtracts corresponding baseline) had the lowest and the highest standard errors, respectively. With respect to Akaike Information Criterion (AIC), the fifth model (comparison of post-treatment values adjusted by all available baseline data) and the eighth model (comparison of post-treatment values adjusted by difference and sum of all available baseline data) had the lowest magnitude, and the ninth model (modeling period baseline jointly with post-treatment values) had the highest AIC for both variables which the values of AIC were 518.1, 520.9 and 1137.8, respectively.Conclusion: To sum up, it is found that baseline data of crossover trial may be used to improve the efficiency of treatment effect estimation when applied appropriately.