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Estimating relative risks and risk differences in randomised controlled trials: a systematic review of current
Jacqueline Thompson1, Samuel I Watson2, Lee Middleton2
1Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, West Midlands, B15 2TT, UK. J.Y.Thompson@bham.ac.uk.
Trials
|January 3, 2025
Summary
Approximately half of randomized controlled trials (RCTs) report covariate-adjusted relative risks or risk differences. Many studies lack detail on estimation methods, with binomial and modified Poisson models being common when reported.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Epidemiology
Background:
- Guidelines recommend reporting covariate-adjusted relative risks and risk differences for binary outcomes in randomized controlled trials (RCTs).
- Various statistical methods exist for estimating these covariate-adjusted effects.
- This study evaluates the application of these methods in high-impact medical journals.
Approach:
- Systematic review of two-arm parallel RCTs published in JAMA, NEJM, Lancet, or BMJ between January 2018 and March 2023.
- Included RCTs reported relative risks or risk differences for a binary primary outcome.
- Data extraction focused on covariate adjustment methods and covariate selection.
Key Points:
- Nearly half (49%) of identified RCTs reported covariate-adjusted relative risks or risk differences.
- Log-binomial and modified Poisson models were most common for adjusted relative risks.
- Binomial models and marginal standardization were frequently used for adjusted risk differences.
Conclusions:
- About 50% of RCTs report covariate-adjusted effect measures, aligning with guidelines.
- A significant number of RCTs provide insufficient methodological detail regarding covariate adjustment.
- When reported, the binomial model, modified Poisson, and marginal standardization are prevalent statistical approaches.
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