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Updated: Jun 5, 2026

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

Issa J Dahabreh1,2,3, James M Robins1,2,3, Sebastien J-P A Haneuse2,3

  • 1CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Statistics in Medicine
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

Generalizing randomized trial findings to target populations requires careful consideration of effect modifiers. New methods for global sensitivity analysis assess how assumption violations impact causal inferences without needing detailed data on unmeasured modifiers.

Keywords:
causal inferencegeneralizabilitysensitivity analysistransportability

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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.