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

arXiv: https://arxiv.org/abs/1905.10684 · HTML: https://mlsanity.com/paper/1905.10684/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2019.

## Built on by (most cited first, 5 of 5)

- [A Review of Generalizability and Transportability](https://mlsanity.com/paper/2102.11904/) (2021)
- [Multiply Robust Federated Estimation of Targeted Average Treatment Effects](https://mlsanity.com/paper/2309.12600/) (2023)
- [Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population](https://mlsanity.com/paper/2401.14512/) (2024)
- [Deep Sensitivity Analysis for Objective-Oriented Combinatorial Optimization](https://mlsanity.com/paper/2403.00016/) (2024)
- [Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumption](https://mlsanity.com/paper/2309.16409/) (2023)

## Abstract (arXiv)

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.