Quantifying clinical relevance

Area of Science:

• Clinical research methodology
• Medical statistics
• Evidence-based medicine

Background:

• P-values alone do not quantify treatment effect size or clinical relevance.
• Standardized effect sizes like Cohen's d can be difficult for clinicians to interpret in practice.
• Effective communication of clinical trial outcomes is crucial for informed medical decision-making.

Purpose of the Study:

• To advocate for the inclusion of specific effect size measures in clinical trial reporting.
• To highlight the utility of Number Needed to Treat (NNT) and Number Needed to Harm (NNH) for clinical application.
• To demonstrate how NNT and NNH can enhance the understanding of treatment effects in clinical practice.

Main Methods:

• Discussion of the limitations of p-values and common effect size measures.
• Introduction and explanation of Number Needed to Treat (NNT) and Number Needed to Harm (NNH).
• Illustrative examples using fictional antipsychotic medications (miracledone, fantastapine) for acute schizophrenia treatment.

Main Results:

• P-values lack information on the magnitude of treatment effects.
• NNT and NNH provide clinically interpretable effect size information for dichotomous outcomes.
• These measures can aid clinicians in evaluating treatment applicability and patient risk/benefit.

Conclusions:

• Effect size measures, particularly NNT and NNH, should be considered for reporting clinical trial results.
• Incorporating NNT and NNH can improve the clinical relevance and interpretability of study findings.
• These metrics support better clinical decision-making by quantifying treatment impact and potential harm.