ArticleBiometrika2025
A general form of covariate adjustment in clinical trials under covariate-adaptive randomization.
Article in Biometrika, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- COADVISE: covariate adjustment with variable selection in randomized controlled trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.Statistics in medicine · 2026Article
- Covariate adjustment in randomized clinical trials: From general theory to practical insights.Clinical trials (London, England) · 2026Review
- Handling incomplete outcomes and covariates in cluster-randomized trials: doubly robust estimation, efficiency considerations, and sensitivity analysis.Biometrics · 2026Article
- Clarifying the role of the Mantel-Haenszel risk difference estimator in randomized clinical trials.Biometrics · 2025Article
- Article
- Negative Control Outcome Adjustment in Early-Phase Randomized Trials: Estimating Vaccine Effects on Immune Responses in HIV Exposed Uninfected Infants.Statistics in medicine · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted estimator, which is a general form of covariate adjustment that uses linear, generalized linear and nonparametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we establish general theorems that show a complete picture of the asymptotic normality, efficiency gain and applicability of augmented inverse propensity weighted estimators. In particular, we provide for the first time a rigorous theoretical justification of using machine learning methods with cross-fitting for dependent data under covariate-adaptive randomization. Based on the general theorems, we offer insights on the conditions for guaranteed efficiency gain and universal applicability under different randomization schemes, which also motivate a joint calibration strategy using some constructed covariates after applying augmented inverse propensity weighted estimators.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.