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Covariate adjustment is an important tool in the analysis of randomizedclinical trials and observational studies. It can be used to increaseefficiency and thus power, and to reduce possible bias. While most statisticaltests in randomized clinical trials are nonparametric in nature, approaches forcovariate adjustment typically rely on specific regression models, such as thelinear model for a continuous outcome, the logistic regression model for adichotomous outcome and the Cox model for survival time. Several recent effortshave focused on model-free covariate adjustment. This paper makes use of theempirical likelihood method and proposes a nonparametric approach to covariateadjustment. A major advantage of the new approach is that it automaticallyutilizes covariate information in an optimal way without fitting nonparametricregression. The usual asymptotic properties, including the Wilks-type result ofconvergence to a chi-square distribution for the empirical likelihood ratiobased test, and asymptotic normality for the corresponding maximum empiricallikelihood estimator, are established. It is also shown that the resulting testis asymptotically most powerful and that the estimator for the treatment effectachieves the semiparametric efficiency bound. The new method is applied to theGlobal Use of Strategies to Open Occluded Coronary Arteries (GUSTO)-I trial.Extensive simulations are conducted, validating the theoretical findings.

An Empirical Likelihood Approach to Nonparametric Covariate Adjustment in Randomized Clinical Trials