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

Issa J. Dahabreh, James M. Robins, Sebastien J.P.A. Haneuse, Iman Saeed, Sarah E. Robertson, Elisabeth A. Stuart, Miguel A. Hernán

Research output: Contribution to journalArticlepeer-review

Abstract

Extending (generalizing or transporting) causal inferences from a randomized trial to a target population requires “generalizability” or “transportability” assumptions, which state that randomized and non-randomized 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 do not require detailed background knowledge about specific unknown or unmeasured determinants of the outcome or modifiers of the treatment effect. Instead, our methods directly parameterize violations of the assumptions using bias functions. 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 non-randomized individuals, as well as to nested trial designs, where a clinical trial is embedded within a cohort sampled from the target population. We illustrate the methods using data from a clinical trial comparing treatments for chronic hepatitis C infection.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - May 25 2019

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population'. Together they form a unique fingerprint.

Cite this