Sensitivity analysis for causality in observational studies for regulatory science

TL;DR

This paper emphasizes the importance of sensitivity analysis in causal inference for observational real-world data, providing practical methods and an example to support regulatory science applications.

Contribution

It introduces a structured approach for sensitivity analysis within a causal roadmap, tailored for regulatory use of real-world evidence.

Findings

Demonstrated sensitivity analysis on Nifurtimox effectiveness study

Reviewed practical methods for robustness testing in causal inference

Highlighted importance for regulatory decision-making

Abstract

Recognizing the importance of real-world data (RWD) for regulatory purposes, the United States (US) Congress passed the 21st Century Cures Act1 mandating the development of Food and Drug Administration (FDA) guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data (FIORD) conducted a meeting bringing together various stakeholder groups to build consensus around best practices for the use of RWD to support regulatory science. Our companion paper describes in detail the context and discussion carried out in the meeting, which includes a recommendation to use a causal roadmap for complete pre-specification of study designs using RWD. This article discusses one step of the roadmap: the specification of a procedure for sensitivity analysis, defined as a procedure for testing the robustness of substantive conclusions to violations of assumptions made in the causal roadmap. We include a worked-out example of a sensitivity analysis from a RWD study on the effectiveness of Nifurtimox in treating Chagas disease, as well as an overview of various methods available for sensitivity analysis in causal inference, emphasizing practical considerations on their use for regulatory purposes.