Connecting Instrumental Variable methods for causal inference to the Estimand Framework
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Instrumental Variables (IV) methods are gaining increasing prominence in the pharmaceutical arena 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 act to distort the interpretation of a treatment effect estimate at a trial's conclusion. IV methods have been used extensively in 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 Principal Stratum and Hypothetical `estimand strategies' proposed 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 a recent pharmaceutical study to further motivate and clarify the ideas.
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