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In randomized clinical trials, adjusting for baseline covariates has beenadvocated as a way to improve credibility and efficiency for demonstrating andquantifying treatment effects. This article studies the augmented inversepropensity weighted (AIPW) estimator, which is a general form of covariateadjustment that includes approaches using linear and generalized linear modelsand machine learning models. Under covariate-adaptive randomization, weestablish a general theorem that shows a complete picture about the asymptoticnormality, efficiency gain, and applicability of AIPW estimators. Based on thegeneral theorem, we provide insights on the conditions for guaranteedefficiency gain and universal applicability under different randomizationschemes, which also motivate a joint calibration strategy using someconstructed covariates after applying AIPW. We illustrate the application ofthe general theorem with two examples, the generalized linear model and themachine learning model. We provide the first theoretical justification of usingmachine learning methods with dependent data under covariate-adaptiverandomization. Our methods are implemented in the R package RobinCar.

A General Form of Covariate Adjustment in Randomized Clinical Trials