Planning a method for covariate adjustment in individually randomised trials: a practical guide

Morris, Tim P.; Walker, A. Sarah; Williamson, Elizabeth J.; White, Ian R.

doi:10.1186/s13063-022-06097-z

Cited by 99Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesStatistical Methods and Bayesian Inference

Planning a method for covariate adjustment in individually randomised trials: a practical guide

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Morris, Tim P., Walker, A. Sarah, Williamson, Elizabeth J., White, Ian R.·2022·Trials

DOI 10.1186/s13063-022-06097-zPMC PMC9014627

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Abstract

Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested , a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service. Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely.

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Adjusting for baseline covariates, including those used in stratified randomisation, can lead to improved power to detect an intervention effect, particularly when a baseline covariate is prognostic of the outcome.
“Adjusting for baseline covariates, including those used in any stratified randomisation, is often advised in the analysis, particularly when a baseline covariate is prognostic of the outcome, as it can lead to improved power to detect an intervention effect.408 409 410”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [409]

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A comparison of covariate adjustment approaches under model misspecification in individually randomized trials
Tackney, M. S., Morris, T. P., White, I. R., Leyrat, C., Diaz‐Ordaz, K., & Williamson, E. (2023) · Trials · doi:10.1186/s13063-022-06967-6 · Cited by 34 · see citing works
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Covariate Adjustment in Analyzing Randomized Clinical Trials: Approaches, Software, and Application
Liu, J., Wang, X., & Pang, H. (2024) · Springer eBooks · doi:10.1007/978-3-031-65937-9_14 · Cited by 1 · see citing works
Covariate adjustment in randomized clinical trials: From general theory to practical insights
Bannick, M. S., Yi, Y., & Ye, T. (2026) · Clinical Trials · doi:10.1177/17407745261442586
Should We Adjust for Baseline Covariates in Randomized Clinical Trials?
Gueorguieva, R. V. (2026) · The Journal of Nervous and Mental Disease · doi:10.1097/nmd.0000000000001876
When does adjusting covariate under randomization help? A comparative study on current practices
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Authors: Morris, T. P., Walker, A. S., Williamson, E. J., & White, I. R.
Affiliations: London School of Hygiene & Tropical Medicine (GB)
MRC Clinical Trials Unit at UCL (GB)
Corresponding author: Morris, Tim P.
ORCID: Morris, Tim P.
Walker, A. Sarah
Williamson, Elizabeth J.
White, Ian R.
Venue: Trials
Publisher: Springer Science and Business Media LLC
Published: 2022-04-18
Volume: 23
Issue: 1
Pages: 328-328
ISSN: 1745-6215 (ISSN-L)
DOI: 10.1186/s13063-022-06097-z
PMC: PMC9014627
OpenAlex: W3180549268
Cited by: 97
References: 47
Citations by year: 2026: 17 · 2025: 28 · 2024: 29 · 2023: 17 · 2022: 5 · 2021: 1
FWCI: 15.12
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Trials (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
UCL Discovery (University College London) (submittedVersion)
Funders: medical research council (MC_UU_12023/21, MC_UU_12023/29, MR/L004933/2/N96, MR/S01442X/1)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: covariate adjustment, baseline covariates, statistical analysis plan, standardization, inverse probability of treatment weighting, robust standard errors, missing covariates, missing outcome data, treatment effect estimation
MeSH terms: Humans, Probability, Research Design, Sample Size

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