A causal framework for classical statistical estimands in failure time settings with competing events

TL;DR

This paper introduces a formal causal framework for classical estimands in failure time analysis with competing risks, clarifying causal interpretations and assumptions using counterfactuals and causal diagrams.

Contribution

It provides a counterfactual-based characterization of classical estimands in competing risks, clarifying causal interpretations and assumptions.

Findings

Distinguishes total and direct effects based on how competing events are defined.

Shows that hazard contrasts generally lack causal interpretability.

Uses causal diagrams to represent assumptions and identify effects.

Abstract

In failure-time settings, a competing risk event is any event that makes it impossible for the event of interest to occur. For example, cardiovascular disease death is a competing event for prostate cancer death because an individual cannot die of prostate cancer once he has died of cardiovascular disease. Various statistical estimands have been defined as possible targets of inference in the classical competing risks literature. Many reviews have described these statistical estimands and their estimating procedures with recommendations about their use. However, this previous work has not used a formal framework for characterizing causal effects and their identifying conditions, which makes it difficult to interpret effect estimates and assess recommendations regarding analytic choices. Here we use a counterfactual framework to explicitly define each of these classical estimands. We clarify that, depending on whether competing events are defined as censoring events, contrasts of risks can define a total effect of the treatment on the event of interest, or a direct effect of the treatment on the event of interest not mediated through the competing event. In contrast, regardless of whether competing events are defined as censoring events, counterfactual hazard contrasts cannot generally be interpreted as causal effects. We illustrate how identifying assumptions for all of these counterfactual estimands can be represented in causal diagrams in which competing events are depicted as time-varying covariates. We present an application of these ideas to data from a randomized trial designed to estimate the effect of estrogen therapy on prostate cancer mortality.