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Putting clinical studies into better perspective by defining accuracy when effect size is small.
1Department of Medicine, Section of Endocrinology, Metabolism and Diabetes, United States; University of Louisville, Louisville, KY 40202, United States.
Clinica Chimica Acta; International Journal of Clinical Chemistry
|October 19, 2025
Summary
Statistical analysis of large datasets can yield significant p-values but poor clinical relevance. This study demonstrates converting relative risk measures into absolute parameters for better clinical decision-making and accuracy assessment.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Health Informatics
Background:
- Advancements in computational power enable analysis of large observational datasets.
- Large sample sizes can lead to statistically significant results with modest effect sizes, potentially causing clinical uncertainty.
- Small effect sizes in studies may be unreliable due to confounders, selection bias, and statistical model limitations.
Purpose of the Study:
- To demonstrate the translation of relative risk indices (e.g., risk ratio, odds ratio) into absolute parameters.
- To enhance the assessment of clinical usefulness and accuracy of study findings.
- To provide methods for better decision-making by healthcare providers.
Main Methods:
- Review of techniques for interpreting statistical results in a clinical context.
- Description of calculations for converting relative statistics (risk, hazard, odds ratios) into absolute terms.
- Utilization of diagnostic statistics and Number Needed to Treat/Expose (NNT/NNE) for practical perspective.
Main Results:
- Statistically significant findings with small effect sizes require careful scrutiny.
- Relative risk measures can be converted to absolute parameters for improved interpretability.
- Diagnostic statistics and NNT/NNE offer a more practical evaluation of study outcomes.
Conclusions:
- The conversion of relative risk measures to absolute parameters aids in evaluating study accuracy.
- Small effect size studies, though statistically significant, should be interpreted with caution.
- Enhanced interpretation of statistical results improves clinical decision-making and practical application.
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