Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population

Stat Med. 2023 Jun 15;42(13):2029-2043. doi: 10.1002/sim.9550. Epub 2023 Feb 27.


Extending (i.e., generalizing or transporting) causal inferences from a randomized trial to a target population requires assumptions that randomized and nonrandomized individuals are exchangeable conditional on baseline covariates. These assumptions are made on the basis of background knowledge, which is often uncertain or controversial, and need to be subjected to sensitivity analysis. We present simple methods for sensitivity analyses that directly parameterize violations of the assumptions using bias functions and do not require detailed background knowledge about specific unknown or unmeasured determinants of the outcome or modifiers of the treatment effect. We show how the methods can be applied to non-nested trial designs, where the trial data are combined with a separately obtained sample of nonrandomized individuals, as well as to nested trial designs, where the trial is embedded within a cohort sampled from the target population.

Keywords: bias analysis; double robustness; g-formula; generalizability; inverse probability weighting; sensitivity analysis; transportability.

Publication types

  • Randomized Controlled Trial
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias
  • Causality
  • Humans
  • Research Design*