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.

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🔍 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.