  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
