Causal treatment effect decompositions with time-to-event outcomes under competing events

TL;DR

This paper introduces a causal decomposition method for treatment effects on time-to-event outcomes considering competing events, revealing mechanisms behind observed effects and enabling clearer causal interpretation.

Contribution

It proposes a novel four-way decomposition of treatment effects accounting for competing events, based on a causal model and counterfactual estimands.

Findings

Decomposition distinguishes four mechanisms influencing treatment effects.

Method applied successfully to datasets from two randomized trials.

Provides a framework for causal interpretation in complex survival analyses.

Abstract

Inference about treatment effects for time-to-event outcomes is often obscured by the presence of competing events. A particularly complex situation arises when the treatment influences the occurrence of the competing event. A comprehensive assessment should then account for different mechanisms by which the treatment and the competing event together produce the apparent treatment effect. Here, we propose a decomposition of the treatment's effect on the event of interest (target), characterising how it arises due to four distinct mechanisms involving both the target and competing events. Based on a causal model, the decomposition relies on cross-world estimands reflecting counterfactual scenarios in which the treatment affects the two events as if set to conflicting levels. We specify exchangeability and consistency assumptions under which the decomposition can be estimated from observed data. We discuss how the new decomposition reveals the role of the competing event and serves as a basis for defining causally interpretable estimands in the presence of competing events. Finally, we demonstrate the use of the four-way decomposition with datasets from two randomised trials.