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Int. J. Environ. Res. Public Health 2016, 13(4), 414; doi:10.3390/ijerph13040414

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

1,†
,
2,†
and
2,*
1
Department of Economics, University of Waterloo, Hagey Hall of Humanities, Waterloo, ON N2L 3G1, Canada
2
Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, 4000 Reservoir Road NW, Washington, DC 20057, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Igor Burstyn
Received: 7 September 2015 / Revised: 1 April 2016 / Accepted: 1 April 2016 / Published: 11 April 2016
(This article belongs to the Special Issue Methodological Innovations and Reflections-1)
View Full-Text   |   Download PDF [288 KB, uploaded 11 April 2016]

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. View Full-Text
Keywords: randomized controlled trials; ANCOVA; empirical likelihood; exponential tilting; continuous updated estimator; generalized empirical likelihood randomized controlled trials; ANCOVA; empirical likelihood; exponential tilting; continuous updated estimator; generalized empirical likelihood
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chaussé, P.; Liu, J.; Luta, G. A Simulation-Based Comparison of Covariate Adjustment Methods for the Analysis of Randomized Controlled Trials. Int. J. Environ. Res. Public Health 2016, 13, 414.

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