Translating questions to estimands in randomized clinical trials with intercurrent events

TL;DR

This paper discusses how to formulate meaningful estimands in randomized clinical trials with intercurrent events, emphasizing decision-oriented questions and proposing alternative causal estimands that require fewer assumptions.

Contribution

It introduces a decision-focused framework for defining estimands in trials with intercurrent events, proposing alternatives like conditional and separable effects that are more practically interpretable.

Findings

Proposes estimands aligned with decision-making and drug development.

Illustrates alternative estimands with five clinical trial examples.

Shows proposed estimands require less stringent assumptions than traditional ones.

Abstract

Intercurrent (post-treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events. A naive conditioning on intercurrent events does not have a straight-forward causal interpretation, and the practical relevance of other commonly used approaches is debated. In this work, we discuss how to formulate and choose an estimand, beyond the marginal intention to treat effect, from the point of view of a decision maker and drug developer. In particular, we argue that careful articulation of a practically useful research question should either reflect decision making at this point in time or future drug development. Indeed, a substantially interesting estimand is a formalization of the (plain English) description of a research question. A common feature of estimands that are practically useful is that they correspond to possibly hypothetical but well-defined interventions in identifiable (sub)populations. To illustrate our points, we consider five examples that were recently used to motivate consideration of principal stratum estimands in clinical trials. In all of these examples, we propose alternative causal estimands, such as conditional effects, sequential regime effects and separable effects, that correspond to explicit research questions of substantial interest. Certain questions require stronger assumptions for identification. However, we highlight that our proposed estimands require less stringent assumptions than estimands commonly targeted in these settings, including principal stratum effects.