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

## TL;DR

This paper introduces simple bias function-based sensitivity analysis methods for assessing the robustness of causal inferences extended from randomized trials to broader populations, applicable to various study designs.

## Contribution

It provides new sensitivity analysis techniques that do not require detailed background knowledge, enabling evaluation of assumptions in generalizability and transportability of trial results.

## Key findings

- Methods applied to hepatitis C trial data demonstrate practical utility.
- Sensitivity analyses reveal the impact of assumption violations on causal estimates.
- Applicable to both nested and non-nested trial designs.

## 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.