Causal inference with recurrent and competing events

TL;DR

This paper develops a formal causal inference framework for recurrent and competing events, clarifying the interpretation of estimands and establishing conditions for their identification from observed data.

Contribution

It formalizes causal estimands in recurrent event settings with competing risks, linking classical statistical measures to causal interpretations, and introduces new estimands relevant for clinical research.

Findings

Causal estimands are clarified and formalized.

Identification conditions are derived using causal graphs.

Discrete-time estimands converge to continuous-time counterparts.

Abstract

Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports injuries in athletes. Competing events, such as death, complicate causal inference in studies of recurrent events because once a competing event occurs, an individual cannot have more recurrent events. Several statistical estimands have been studied in recurrent event settings, with and without competing events. However, the causal interpretations of these estimands, and the conditions that are required to identify these estimands from observed data, have yet to be formalized. Here we use a formal framework for causal inference to formulate several causal estimands in recurrent event settings, with and without competing events. We clarify when commonly used classical statistical estimands can be interpreted as causal quantities from the causal mediation literature, such as (controlled) direct effects and total effects. Furthermore, we show that recent results on interventionist mediation estimands allow us to define new causal estimands with recurrent and competing events that may be of particular clinical relevance in many subject matter settings. We use causal directed acyclic graphs and single world intervention graphs to illustrate how to reason about identification conditions for the various causal estimands based on subject matter knowledge. Furthermore, using results on counting processes, we show that our causal estimands and their identification conditions, which are articulated in discrete time, converge to classical continuous time counterparts in the limit of fine discretizations of time.