Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population.
IJ, D., JM, R., SJA, H., SE, R., JA, S., & MA, H. (2026). Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population.. Statistics in medicine. https://doi.org/10.1002/sim.70083
IJ D, JM R, SJA H, SE R, JA S, MA H. Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population.. Statistics in medicine. 2026; doi: 10.1002/sim.70083
IJ D, JM R, SJA H, et al. Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population.[J]. Statistics in medicine. 2026. DOI: 10.1002/sim.70083.
@article{ij2026,
author = {Dahabreh IJ and Robins JM and Haneuse SJA and Robertson SE and Steingrimsson JA and Hernán MA},
title = {Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population.},
journal = {Statistics in medicine},
year = {2026},
doi = {10.1002/sim.70083},
note = {PMID: 42237885},
}
TY - JOUR AU - Dahabreh IJ AU - Robins JM AU - Haneuse SJA AU - Robertson SE AU - Steingrimsson JA AU - Hernán MA TI - Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population. T2 - Statistics in medicine PY - 2026 DO - 10.1002/sim.70083 AN - PMID:42237885 ER -
When individuals participating in a randomized trial differ with respect to the distribution of effect modifiers compared with the target population where the trial results will be used, treatment effect estimates from the trial may not directly apply to target population. Methods for extending-generalizing or transporting-causal inferences from the trial to the target population rely on conditional exchangeability assumptions between randomized and non-randomized individuals. The validity of these assumptions is often uncertain or controversial and investigators need to examine how violation of the assumptions would impact study conclusions. We describe methods for global sensitivity analysis that directly parameterize violations of the assumptions in terms of potential (counterfactual) outcome distributions. Our approach does not require detailed knowledge about the distribution of specific unmeasured effect modifiers or their relationship with the observed variables. We illustrate the methods using data from a trial nested within a cohort of trial-eligible individuals to compare coronary artery surgery plus medical therapy versus medical therapy alone for stable ischemic heart disease.