Dealing with multiple intercurrent events using hypothetical and treatment policy strategies simultaneously

TL;DR

This paper explores defining treatment effects in clinical trials when multiple intercurrent events are handled with different strategies, using causal diagrams and potential outcomes, highlighting various assumptions and estimation implications.

Contribution

It introduces a framework for simultaneously addressing multiple intercurrent events with different strategies using causal diagrams and potential outcomes.

Findings

Different causal estimand definitions depend on assumptions.

Simulation study illustrates estimation implications.

Conceptual example with a diabetes trial.

Abstract

To precisely define the treatment effect of interest in a clinical trial, the ICH E9 estimand addendum describes that relevant so-called intercurrent events should be identified and strategies specified to deal with them. Handling intercurrent events with different strategies leads to different estimands. In this paper, we focus on estimands that involve addressing one intercurrent event with the treatment policy strategy and another with the hypothetical strategy. We define these estimands using potential outcomes and causal diagrams, considering the possible causal relationships between the two intercurrent events and other variables. We show that there are different causal estimand definitions and assumptions one could adopt, each having different implications for estimation, which is demonstrated in a simulation study. The different considerations are illustrated conceptually using a diabetes trial as an example.