Connecting Instrumental Variable methods for causal inference to the Estimand Framework

TL;DR

This paper reviews how instrumental variable methods can be integrated with the estimand framework in clinical trials to improve causal inference, especially in the presence of intercurrent events, with practical implementation guidance.

Contribution

It connects IV methods with the estimand framework in clinical trials, providing detailed mapping, assumptions, and implementation strategies for pharmaceutical applications.

Findings

Mapping IV approaches to estimand strategies clarifies causal inference.

Simulation studies demonstrate practical application in pharmaceutical trials.

Discussion of assumptions and sensitivity analyses enhances robustness of causal estimates.

Abstract

Causal inference methods are gaining increasing prominence in pharmaceutical drug development in light of the recently published addendum on estimands and sensitivity analysis in clinical trials to the E9 guideline of the International Council for Harmonisation. The E9 addendum emphasises the need to account for post-randomization or `intercurrent' events that can potentially influence the interpretation of a treatment effect estimate at a trial's conclusion. Instrumental Variables (IV) methods have been used extensively in economics, epidemiology and academic clinical studies for `causal inference', but less so in the pharmaceutical industry setting until now. In this tutorial paper we review the basic tools for causal inference, including graphical diagrams and potential outcomes, as well as several conceptual frameworks that an IV analysis can sit within. We discuss in detail how to map these approaches to the Treatment Policy, Principal Stratum and Hypothetical `estimand strategies' introduced in the E9 addendum, and provide details of their implementation using standard regression models. Specific attention is given to discussing the assumptions each estimation strategy relies on in order to be consistent, the extent to which they can be empirically tested and sensitivity analyses in which specific assumptions can be relaxed. We finish by applying the methods described to simulated data closely matching two recent pharmaceutical trials to further motivate and clarify the ideas