Efficient baseline utilization for incomplete block crossover clinical trials. (March 2019)

• Record Type: Journal Article
• Authors: Jemielita, Thomas O; Putt, Mary E; Mehrotra, Devan V
• Is Part Of: Statistical methods in medical research. Volume 28:Number 3(2019)
• Page Start: 801
• Page End: 821
• Publication Date: 2019-03
• Subjects: Baseline adjustment -- covariate -- covariance structure -- crossover trial -- longitudinal analysis -- incomplete block design; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217736790
• Languages: English
• ISSNs: 0962-2802

Abstract

Incomplete block crossover trials with period-specific baseline and post-baseline (outcome) measures for each subject are often used in clinical drug development; without loss of generality, we focus on the three-treatment two-period (3 × 2 ) crossover. Data from such trials are commonly analyzed using a mixed effects model with indicator terms for treatment and period, and an unstructured covariance matrix for the vector of intra-subject measurements. It is well-known that treatment effect estimates from this analysis are complex functions of both within-subject and between-subject treatment contrasts. We caution that the associated type I error rate and power for hypothesis testing can be non-trivially influenced by how the baselines are utilized. Specifically, the mixed effects analysis which uses change from baseline as the dependent variable is shown to consistently underperform corresponding analyses in which the outcome is the dependent variable and linear combinations of the baselines are used as period-specific and/or period-invariant covariates. A simpler fixed effects analysis of covariance involving only within-subject contrasts is also described for small sample situations in which the mixed effects analyses can suffer from increased type I error rates. Theoretical insights, simulation results and an illustrative example with real data are used to develop the main points.