Addressing Positivity Violations in Extending Inference to a Target Population

TL;DR

This paper proposes a framework to address positivity violations in extending trial results to target populations, improving external validity and robustness of causal inferences in real-world settings.

Contribution

It introduces a novel method combining group characterization and sensitivity analysis to handle positivity violations in population inference.

Findings

Applied to opioid use disorder trials with real-world data

Enhanced robustness of treatment effect estimates

Identified sampling limitations in trial populations

Abstract

Enhancing the external validity of trial results is essential for their applicability to real-world populations. However, violations of the positivity assumption can limit both the generalizability and transportability of findings. To address positivity violations in estimating the average treatment effect for a target population, we propose a framework that integrates characterizing the underrepresented group and performing sensitivity analysis for inference in the original target population. Our approach helps identify limitations in trial sampling and improves the robustness of trial findings for real-world populations. We apply this approach to extend findings from phase IV trials of treatments for opioid use disorder to a real-world population based on the 2021 Treatment Episode Data Set.