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The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies
Open accessAbstract
BACKGROUND: Adjustment for prognostic covariates can lead to increased power in the analysis of randomized trials. However, adjusted analyses are not often performed in practice. METHODS: We used simulation to examine the impact of covariate adjustment on 12 outcomes from 8 studies across a range of therapeutic areas. We assessed (1) how large an increase in power can be expected in practice; and (2) the impact of adjustment for covariates that are not prognostic. RESULTS: Adjustment for known prognostic covariates led to large increases in power for most outcomes. When power was set to 80% based on an unadjusted analysis, covariate adjustment led to a median increase in power to 92.6% across the 12 outcomes (range 80.6 to 99.4%). Power was increased to over 85% for 8 of 12 outcomes, and to over 95% for 5 of 12 outcomes. Conversely, the largest decrease in power from adjustment for covariates that were not prognostic was from 80% to 78.5%. CONCLUSIONS: Adjustment for known prognostic covariates can lead to substantial increases in power, and should be routinely incorporated into the analysis of randomized trials. The potential benefits of adjusting for a small number of possibly prognostic covariates in trials with moderate or large sample sizes far outweigh the risks of doing so, and so should also be considered.
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“In addition to adjusting for baseline to end-of-treatment (EOT) by group interactions, we adjusted for baseline depression, a well-established prognostic covariate of chronic pain [28], to reduce baseline differences in the outcome variables [29].”
From Yoga is effective for treating chronic pain in veterans with Gulf War Illness at long-term follow-up (2023) · cites this paper as [29]
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“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 [408]
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“However, under the assumptions of an unconfounded experimental design with batches containing both controls and cases, batch effects can be removed by adding a batch covariate to the regression model71. This increases the power compared to non-batch corrected analyses72–74.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [74]
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Metadata
- Authors
- Kahan, B. C., Jairath, V., Doré, C. J., & Morris, T. P.
- Affiliations
- Queen Mary University of London (GB)
- University of Oxford (GB)
- University College London (GB)
- MRC Clinical Trials Unit at UCL (GB)
- Corresponding author
- Kahan, Brennan C
- Venue
- Trials
- Published
- 2014-04-23
- Volume
- 15
- Issue
- 1
- Pages
- 139-139
- ISSN
- 1745-6215 (ISSN-L)
- PMC
- PMC4022337
- OpenAlex
- W2166091513
- Cited by
- 497
- References
- 52
- Citations by year
- 2026: 31 · 2025: 69 · 2024: 61 · 2023: 67 · 2022: 73 · 2021: 57 · 2020: 37 · 2019: 22 · 2018: 26 · 2017: 16 · 2016: 20 · 2015: 11 · 2014: 6
- FWCI
- 18.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Trials (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- Funders
- National Institute for Health and Care Research (CL-2013-13-008)
- Medical Research Council (MC_EX_G0800814)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- covariate adjustment, prognostic factors, statistical power, simulation study, trial analysis
- MeSH terms
- Analysis of Variance, Computer Simulation, Data Interpretation, Statistical, Humans, Research Design, Reproducibility of Results, Models, Statistical, Linear Models, Logistic Models, Proportional Hazards Models, Randomized Controlled Trials as Topic
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