A Simulation-Based Comparison of Covariate Adjustment Methods for the Analysis of Randomized Controlled Trials

Pierre Chaussé, Jin Liu and George Luta
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Pierre Chaussé: Department of Economics, University of Waterloo, Hagey Hall of Humanities, Waterloo, ON N2L 3G1, Canada
Jin Liu: Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, 4000 Reservoir Road NW, Washington, DC 20057, USA
George Luta: Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, 4000 Reservoir Road NW, Washington, DC 20057, USA

IJERPH, 2016, vol. 13, issue 4, 1-15

Abstract: Covariate adjustment methods are frequently used when baseline covariate information is available for randomized controlled trials. Using a simulation study, we compared the analysis of covariance (ANCOVA) with three nonparametric covariate adjustment methods with respect to point and interval estimation for the difference between means. The three alternative methods were based on important members of the generalized empirical likelihood (GEL) family, specifically on the empirical likelihood (EL) method, the exponential tilting (ET) method, and the continuous updated estimator (CUE) method. Two criteria were considered for the comparison of the four statistical methods: the root mean squared error and the empirical coverage of the nominal 95% confidence intervals for the difference between means. Based on the results of the simulation study, for sensitivity analysis purposes, we recommend the use of ANCOVA (with robust standard errors when heteroscedasticity is present) together with the CUE-based covariate adjustment method.

Keywords: randomized controlled trials; ANCOVA; empirical likelihood; exponential tilting; continuous updated estimator; generalized empirical likelihood (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
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