Understanding Subgroup and Sensitivity Analyses in Clinical Research

 

🔍 Subgroup Analysis

Purpose: To explore whether the treatment effect varies across different subsets of the study population.

✅ Common Subgroups:

  • Age groups (e.g., children vs. adults)
  • Sex (male vs. female)
  • Disease severity
  • Geographic region
  • Genetic markers

🔎 Why it's done:

  • To identify heterogeneity of treatment effects
  • To support personalized medicine
  • To generate hypotheses for future studies
  • To check consistency of results across groups

⚠️ Subgroup analyses are often exploratory and should be interpreted cautiously, especially if not pre-specified.

🔍 Sensitivity Analysis

Purpose: To test the robustness of the main findings by varying assumptions, methods, or data inputs.

✅ Common Scenarios:

  • Handling missing data (e.g., using multiple imputation vs. complete case analysis)
  • Changing inclusion/exclusion criteria
  • Using alternative statistical models
  • Excluding outliers or protocol deviations

🔎 Why it's done:

  • To assess how sensitive results are to changes in methodology
  • To increase confidence in the primary findings
  • To identify potential biases or limitations

Sensitivity analysis helps ensure that conclusions are not overly dependent on specific assumptions or data quirks.

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