Clinical versus statistical significance
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Clinical significance vs. statistical significance
In Randomized Controlled Trials (RCTs), interpreting results requires understanding both statistical significance and clinical significance. While they are related, they address different questions: one about the likelihood of an effect being real, and the other about whether that effect matters in practice.
1. Statistical Significance
Definition: Statistical significance tests whether an observed effect is likely due to chance.
Key Features
- Determined using p-values and confidence intervals (CIs).
- A p-value < 0.05 is commonly used to denote significance, implying a less than 5% probability that the effect occurred by chance.
- Confidence intervals provide a range of plausible values for the effect; if the CI excludes the null (e.g., 0 for mean difference, 1 for risk ratio), the result is statistically significant.
Example:
A new drug reduces systolic blood pressure by 5 mmHg compared to placebo (p = 0.03, 95% CI: 1.2–8.8). → Interpretation: The result is statistically significant since p < 0.05 and the CI does not include 0.
Limitations
- Does not indicate whether the result is clinically meaningful.
- Strongly influenced by sample size—small effects can appear statistically significant in large trials and vice versa.
2. Clinical Significance
Definition: Clinical significance assesses whether an effect is meaningful and beneficial in real-world patient care.
Key Features
- Focuses on effect size and relevance to patient outcomes, not just p-values.
- Often judged using the Minimal Clinically Important Difference (MCID), the smallest difference patients or clinicians consider beneficial.
- MCID is based on previous literature, expert consensus, or patient-reported outcomes.
Example:
A new drug reduces systolic blood pressure by 5 mmHg, but the MCID is 10 mmHg. → Interpretation: The effect is statistically significant but not clinically significant.
Limitations
- Involves clinical judgment and may vary by setting, population, or outcome.
- No universally accepted thresholds for all outcomes.
3. Comparing Statistical vs. Clinical Significance
| Aspect | Statistical Significance | Clinical Significance |
|---|---|---|
| Definition | Likelihood that an effect is not due to chance | Whether the effect is meaningful for patients |
| Measure | p-value, confidence interval | Minimal Clinically Important Difference (MCID) |
| Depends on | Sample size, variability | Patient outcomes, clinical judgment |
| Limitations | May detect small, unimportant differences | Can be subjective, lacks universal thresholds |
| Example | p = 0.02 for a 2 mmHg BP reduction | A 10 mmHg reduction is considered clinically important |
4. Best Practices in RCTs
- Report both statistical and clinical significance for a complete interpretation.
- Consider effect size measures (e.g., Cohen’s d, risk difference, odds ratio).
- Present confidence intervals to show precision and plausible ranges.
- Predefine the MCID in protocols and analysis plans.
Conclusion
- Statistical significance does not imply clinical importance.
- RCTs should prioritize outcomes that are meaningful for patients, not just statistically detectable.
- Reporting and interpreting both types of significance supports better decision-making in clinical and policy settings.
Bibliography
- Feinstein AR. The unit of analysis in epidemiology: the fallacy of comparing groups when individuals are the goal. American Journal of Epidemiology. 1985;121(1):1–8.
- Guyatt GH, Jaeschke R, Heddle N, et al. Basic statistics for clinicians: 2. Interpreting study results: confidence intervals. CMAJ. 1995;152(2):169–173.
- Jaeschke R, Singer J, Guyatt GH. Measurement of health status: ascertaining the minimal clinically important difference. Controlled Clinical Trials. 1989;10(4 Suppl):407S–415S.
- Sackett DL, Haynes RB, Guyatt GH, Tugwell P. Clinical Epidemiology: A Basic Science for Clinical Medicine. 2nd ed. Boston: Little, Brown and Company; 1991. Chapter 5: Statistical versus clinical significance.
- Goodman SN. Toward evidence-based medical statistics. 2: The Bayes factor. Annals of Internal Medicine. 1999;130(12):1005–1013.
Adapted for educational use. Please cite relevant trial methodology sources when using this material in research or teaching.