 Regression‐based multiple treatment effect estimation under covariate‐adaptive randomizationYujia Gu, Hanzhong Liu and Wei Ma Biometrics, 2023, vol. 79, issue 4, 2869-2880 Abstract: Covariate‐adaptive randomization methods are widely used in clinical trials to balance baseline covariates. Recent studies have shown the validity of using regression‐based estimators for treatment effects without imposing functional form requirements on the true data generation model. These studies have had limitations in certain scenarios; for example, in the case of multiple treatment groups, these studies did not consider additional covariates or assumed that the allocation ratios were the same across strata. To address these limitations, we develop a stratum‐common estimator and a stratum‐specific estimator under multiple treatments. We derive the asymptotic behaviors of these estimators and propose consistent nonparametric estimators for asymptotic variances. To determine their efficiency, we compare the estimators with the stratified difference‐in‐means estimator as the benchmark. We find that the stratum‐specific estimator guarantees efficiency gains, regardless of whether the allocation ratios across strata are the same or different. Our conclusions were also validated by simulation studies and a real clinical trial example. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:79:y:2023:i:4:p:2869-2880 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Biometrics from The International Biometric Society |
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