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A comparison of covariate adjustment approaches under model misspecification in individually randomized trials

Tackney, Mia S; Morris, Tim; White, Ian; Leyrat, Clemence; Diaz-Ordaz, Karla; Williamson, Elizabeth; (2023) A comparison of covariate adjustment approaches under model misspecification in individually randomized trials. Trials , 24 (1) , Article 14. 10.1186/s13063-022-06967-6. Green open access

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Abstract

Adjustment for baseline covariates in randomized trials has been shown to lead to gains in power and can protect against chance imbalances in covariates. For continuous covariates, there is a risk that the the form of the relationship between the covariate and outcome is misspecified when taking an adjusted approach. Using a simulation study focusing on individually randomized trials with small sample sizes, we explore whether a range of adjustment methods are robust to misspecification, either in the covariate-outcome relationship or through an omitted covariate-treatment interaction. Specifically, we aim to identify potential settings where G-computation, inverse probability of treatment weighting (IPTW), augmented inverse probability of treatment weighting (AIPTW) and targeted maximum likelihood estimation (TMLE) offer improvement over the commonly used analysis of covariance (ANCOVA). Our simulations show that all adjustment methods are generally robust to model misspecification if adjusting for a few covariates, sample size is 100 or larger, and there are no covariate-treatment interactions. When there is a non-linear interaction of treatment with a skewed covariate and sample size is small, all adjustment methods can suffer from bias; however, methods that allow for interactions (such as G-computation with interaction and IPTW) show improved results compared to ANCOVA. When there are a high number of covariates to adjust for, ANCOVA retains good properties while other methods suffer from under- or over-coverage. An outstanding issue for G-computation, IPTW and AIPTW in small samples is that standard errors are underestimated; they should be used with caution without the availability of small-sample corrections, development of which is needed. These findings are relevant for covariate adjustment in interim analyses of larger trials.

Type: Article
Title: A comparison of covariate adjustment approaches under model misspecification in individually randomized trials
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/s13063-022-06967-6
Publisher version: https://doi.org/10.1186/s13063-022-06967-6
Language: English
Additional information: © 2023 BioMed Central Ltd. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Covariate adjustment, Randomized controlled trials, Misspecification, ANCOVA, G-computation, IPTW, AIPTW, TMLE
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10163082
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