A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events

Abstract

In randomized clinical trials, stopping study medication, use of rescue treatment, and other intercurrent events can complicate interpretation of results. The ICH E9(R1) Addendum on estimands stimulated important cross-disciplinary discussions on trial objectives. Unfortunately, with influence of the Addendum, many trials have proposed analyzing primary endpoints using "while on treatment", "hypothetical", or "principal stratum" strategies that handle intercurrent events in ways that use post-randomization outcomes to exclude information from randomized participants and don't preserve integrity of randomization, or that don't reliably capture the intervention's meaningful net effects. These approaches have inherent limitations in ability to draw scientifically rigorous inference on clinically relevant causal effects important for decisions about adopting interventions. We describe advantages of trials with standard-of-care control arms targeting estimands using "treatment policy" approaches for intercurrent events, while potentially incorporating other meaningful intercurrent events, such as death, into the primary endpoint applying a composite strategy. Well-designed and -conducted trials targeting such estimands achieve scientifically rigorous causal inference through analyzes that protect the integrity of randomization. Such estimands also provide meaningful information relevant to real-world settings because they (1) are unconditional in nature i.e., they don't condition on post-treatment circumstances that might not be many participants' experiences; and (2) properly evaluate the experimental intervention within a regimen that includes possible ancillary care that would be clinically appropriate in real-world settings. We hope to add clarity about appropriate strategies for intercurrent events and how to improve design, conduct, and analysis of clinical trials to address questions of greatest clinical importance reliably.

Keywords: causal inference; estimands; intercurrent events; randomization.