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2016 ; 13
(4
): 414
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A Simulation-Based Comparison of Covariate Adjustment Methods for the Analysis of
Randomized Controlled Trials
#MMPMID27077870
Chaussé P
; Liu J
; Luta G
Int J Environ Res Public Health
2016[Apr]; 13
(4
): 414
PMID27077870
show ga
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.
|*Computer Simulation
[MESH]
|*Data Interpretation, Statistical
[MESH]
|Humans
[MESH]
|Probability
[MESH]
|Randomized Controlled Trials as Topic/*methods
[MESH]