A recommended analysis for 2 × 2 crossover trials with baseline measurements. (17th September 2014)

• Record Type: Journal Article
• Authors: Mehrotra, Devan V.
• Is Part Of: Pharmaceutical statistics. Volume 13:Number 6(2014:Nov./Dec.)
• Page Start: 376
• Page End: 387
• Publication Date: 2014-09-17
• Subjects: Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1638
• Languages: English
• ISSNs: 1539-1604

Abstract

<abstract abstract-type="main" id="pst1638-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="pst1638-para-0001">In many two‐period, two‐treatment (2 × 2) crossover trials, for each subject, a continuous response of interest is measured before and after administration of the assigned treatment within each period. The resulting data are typically used to test a null hypothesis involving the true difference in treatment response means. We show that the power achieved by different statistical approaches is greatly influenced by (i) the 'structure' of the variance–covariance matrix of the vector of within‐subject responses and (ii) how the baseline (i.e., pre‐treatment) responses are accounted for in the analysis. For (ii), we compare different approaches including ignoring one or both period baselines, using a common change from baseline analysis (which we advise against), using functions of one or both baselines as period‐specific or period‐invariant covariates, and doing joint modeling of the post‐baseline and baseline responses with corresponding mean constraints for the latter. Based on theoretical arguments and simulation‐based type I error rate and power properties, we recommend an analysis of covariance approach that uses the within‐subject difference in treatment responses as the dependent variable and the corresponding difference in baseline responses as a covariate. Data from three clinical trials are used to illustrate the main points. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>