Subgroup analysis
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Subgroup analysis
Subgroup analysis in RCTs explores whether treatment effects differ across specific patient groups—such as by age, sex, or disease severity. While such analyses can provide insight into effect heterogeneity, they must be conducted and interpreted carefully to avoid false or misleading conclusions.
1. When to Conduct Subgroup Analysis
- Pre-specified vs. Post-hoc: Pre-specified subgroup analyses—planned before data collection—are more robust and credible. Post-hoc analyses—performed after seeing the results—are considered exploratory and hypothesis-generating.
- Biological plausibility: Subgroup differences should have a scientific rationale based on prior evidence or theory.
- Sufficient sample size: Subgroups should be adequately powered; small subgroup sizes increase the risk of unreliable findings.
2. Common Subgroup Variables
Subgroup analyses are often based on:
- Demographics: Age, sex, race, ethnicity, socioeconomic status
- Clinical characteristics: Disease severity, comorbidities, baseline biomarker levels
- Intervention-related factors: Dosage, duration, adherence level, site of treatment delivery
3. Statistical Methods for Subgroup Analysis
- Interaction terms: Include interaction terms in regression models (e.g., treatment × age group) to test if the treatment effect differs significantly across subgroups.
- Stratified analysis: Estimate the treatment effect within each subgroup separately (e.g., males vs. females).
- Forest plots: Use forest plots to visualize subgroup-specific treatment effects and confidence intervals.
4. Avoiding Common Pitfalls
- Multiple comparisons problem: Testing many subgroups increases the chance of Type I error (false positives). Use statistical adjustments such as Bonferroni correction or Bayesian hierarchical models to address this.
- Over-interpretation: Subgroup findings should be viewed as exploratory unless supported by strong evidence or replication.
- Misclassification: Ensure subgroup definitions are consistent, clinically relevant, and based on valid cut-offs.
5. Reporting Subgroup Analyses
- Clearly distinguish between pre-specified and post-hoc subgroup analyses.
- Report both subgroup-specific estimates and interaction p-values to determine whether differences are statistically significant.
- Follow CONSORT guidelines for subgroup reporting.
Example interpretation:
The treatment effect was greater in younger participants (RR: 1.3, 95% CI: 1.1–1.5) compared to older participants (RR: 1.0, 95% CI: 0.8–1.2), with a significant interaction term (p = 0.03), suggesting age modifies the intervention effect.
Conclusion
- Subgroup analyses should be planned in advance and based on sound clinical rationale.
- Use statistical interaction tests rather than visually comparing subgroup estimates.
- Forest plots and proper reporting enhance transparency and interpretability.
See also
Bibliography
- Sun X, Briel M, Busse JW, et al. Credibility of claims of subgroup effects in randomized controlled trials: systematic review. BMJ. 2012;344:e1553.
- Oxman AD, Guyatt GH. A consumer’s guide to subgroup analyses. Annals of Internal Medicine. 1992;116(1):78–84.
- Kent DM, Rothwell PM, Ioannidis JPA, Altman DG, Hayward RA. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials. 2010;11:85.
- Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. The Lancet. 2000;355(9209):1064–1069.
- Wang R, Lagakos SW, Ware JH, Hunter DJ, Drazen JM. Statistics in medicine—reporting of subgroup analyses in clinical trials. New England Journal of Medicine. 2007;357(21):2189–2194.
Adapted for educational use. Please cite relevant trial methodology sources when using this material in research or teaching.