Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population

Area of Science:

• Epidemiology
• Biostatistics
• Clinical Trials

Background:

• Randomized trials are crucial for evidence-based medicine.
• Treatment effect estimates may not generalize to target populations due to differing effect modifier distributions.
• Assessing the generalizability of trial findings is critical for real-world application.

Purpose of the Study:

• To develop and illustrate methods for global sensitivity analysis to assess the impact of assumption violations on causal inference generalization.
• To evaluate how deviations from conditional exchangeability assumptions affect treatment effect estimates.
• To provide a framework for examining the robustness of trial conclusions when applied to broader populations.

Main Methods:

• Developed global sensitivity analysis methods that parameterize assumption violations using potential (counterfactual) outcome distributions.
• The approach does not require specific knowledge of unmeasured effect modifier distributions or their relationships with observed variables.
• Applied methods to a trial nested within a cohort for coronary artery disease treatment comparison.

Main Results:

• Demonstrated a method to quantify the potential impact of violating conditional exchangeability assumptions on treatment effect generalization.
• The sensitivity analysis provides a direct measure of how unmeasured confounding or effect modification might alter conclusions.
• Illustrative analysis using coronary artery surgery trial data.

Conclusions:

• Global sensitivity analysis offers a robust approach to evaluate the generalizability of randomized trial findings.
• The proposed methods enhance the transparency and reliability of applying trial results to diverse target populations.
• Investigators can better assess the potential impact of unmeasured factors on treatment effect estimates.