The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies

Kahan, Brennan C; Jairath, Vipul; Doré, Caroline J; Morris, Tim P

doi:10.1186/1745-6215-15-139

Cited by 498Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesMeta-analysis and systematic reviews

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The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies

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Kahan, Brennan C, Jairath, Vipul, Doré, Caroline J, Morris, Tim P·2014·Trials

DOI 10.1186/1745-6215-15-139PMC PMC4022337

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Abstract

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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Covariate adjustment had similar benefits in small and large randomized controlled trials
Thompson, D. D., Lingsma, H. F., Whiteley, W. N., Murray, G. D., & Steyerberg, E. W. (2014) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.11.001 · Cited by 42
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The Effect of Covariate Adjustment for Cox Regression in Cardiovascular Outcome Trials
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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
ORCID: Kahan, Brennan C
Jairath, Vipul
Doré, Caroline J
Morris, Tim P
Venue: Trials
Publisher: Springer Science and Business Media LLC
Published: 2014-04-23
Volume: 15
Issue: 1
Pages: 139-139
ISSN: 1745-6215 (ISSN-L)
DOI: 10.1186/1745-6215-15-139
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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The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies

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