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Should We Adjust for Baseline Covariates in Randomized Clinical Trials?
1Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut.
The Journal of Nervous and Mental Disease
|July 16, 2026
Summary
Covariate adjustment in randomized clinical trials (RCTs) can improve precision and power when prespecified and justified. Unadjusted and prespecified adjusted analyses are valid, but post hoc adjustments may introduce bias.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Epidemiology
Background:
- Randomized clinical trials (RCTs) aim to determine intervention effects.
- Prognostic factors influencing outcomes necessitate covariate-adjusted analyses in RCTs.
- Understanding adjustment reasons, benefits, drawbacks, and best practices is crucial.
Purpose of the Study:
- To review guidelines and recommendations for covariate adjustment in RCTs.
- To synthesize current best practices for covariate adjustment.
- To inform methodological rigor and transparency in RCT reporting.
Main Methods:
- Systematic review of guidelines from regulatory bodies (e.g., EMA, FDA).
- Inclusion of recent studies and meta-analyses on covariate adjustment.
- Analysis focused on validity, precision, and potential biases of adjustment methods.
Main Results:
- Unadjusted and prespecified adjusted analyses are valid in RCTs.
- Post hoc covariate adjustments can introduce bias.
- Adjusting for prognostic variables enhances precision and statistical power.
- Methods range from direct adjustment to complex techniques like inverse probability of treatment weighting.
Conclusions:
- Covariate adjustments must be prespecified and theoretically justified.
- Transparent reporting of covariate adjustment is essential.
- Adherence to CONSORT 2025 guidelines promotes methodological rigor and transparency in RCTs.
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