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Published on: January 8, 2020
Clinically useful measures of effect in binary analyses of randomized trials
1Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada.
Journal of Clinical Epidemiology
|August 1, 1994
Summary
Choosing the right treatment effect estimators is crucial for physicians. Reporting both relative risk and risk difference, along with their complements, best informs clinical decisions.
Area of Science:
- Medical Statistics
- Clinical Trial Analysis
- Evidence-Based Medicine
Background:
- Randomized clinical trials report treatment effects using relative and absolute estimators.
- These measures influence physician understanding of treatment effect size and subsequent decisions.
Purpose of the Study:
- Compare different treatment effect estimators for clarity and clinical relevance.
- Identify which clinical questions are best answered by each estimator.
- Highlight potential misinterpretations and mislabeling of estimators.
Main Methods:
- Comparative analysis of relative and absolute treatment effect estimators.
- Evaluation of information conveyed by relative risk, relative risk reduction, risk difference, and number needed to treat.
- Assessment of the odds ratio's utility in conveying clinical information.
Main Results:
- Relative risk reduction and number needed to treat directly address clinically important questions.
- Both single trials and meta-analyses benefit from reporting both relative and absolute effects.
- Odds ratio is not a substitute for risk ratio due to baseline risk influence, especially in high-risk populations.
Conclusions:
- Reporting both relative and absolute effects (relative risk, risk difference, and their complements) provides the most direct clinical information.
- Physicians should be aware of the limitations of estimators like the odds ratio, particularly in high-risk groups.
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