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

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.

authors

Chaussé P,Liu J,Luta G

doi

10.3390/ijerph13040414

subject

Has Abstract

pub_date

2016-04-11 00:00:00

pages

414

issue

4

eissn

1661-7827

issn

1660-4601

pii

ijerph13040414

journal_volume

13

pub_type

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