Causal Inference in the Face of Competing Events
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EPIDEMIOLOGIC METHODS (P HOWARDS, SECTION EDITOR)
Causal Inference in the Face of Competing Events Jacqueline E. Rudolph 1
&
Catherine R. Lesko 2 & Ashley I. Naimi 1
# Springer Nature Switzerland AG 2020
Abstract Purpose of Review Epidemiologists frequently must handle competing events, which prevent the event of interest from occurring. We review considerations for handling competing events when interpreting results causally. Recent Findings When interpreting statistical associations as causal effects, we recommend following a causal inference “roadmap” as one would in an analysis without competing events. There are, however, special considerations to be made for competing events when choosing the causal estimand that best answers the question of interest, selecting the statistical estimand (e.g., the cause-specific or subdistribution) that will target that causal estimand, and assessing whether causal identification conditions (e.g., conditional exchangeability, positivity, and consistency) have been sufficiently met. Summary When doing causal inference in the competing events setting, it is critical to first ascertain the relevant question and the causal estimand that best answers it, with the choice often being between estimands that do and do not eliminate competing events. Keywords Causal inference . Competing events . Survival analysis
Introduction Epidemiologists study the occurrence of health events and the relationship between exposures and those events. We frequently run into scenarios where some of the participants in our study experience an event that prevents them from experiencing the outcome under study. Such events are referred to as competing events. In fact, unless the outcome is all-cause mortality, there will always be a potential competing event for the outcome, namely death (or death from a different cause). There exist well-established statistical methods for timeto-event analyses that allow us to estimate a range of measures of association between an exposure and an outcome, Figures: Fig. 1 was designed by the authors and has not been included in previously published work. This article is part of the Topical Collection on Epidemiologic Methods * Jacqueline E. Rudolph [email protected] 1
Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 DeSoto Street, Pittsburgh, PA 15261, USA
2
Department of Epidemiology, Johns Hopkins School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA
while carefully considering the occurrence of competing events [1–3]. However, interpreting those exposureoutcome associations causally requires a causal inference framework that formalizes conditions under which such a causal interpretation might hold. Counterfactuals (or potential outcomes) form the basis of the modern approach to causal inference [4]. We consider what our outcome might have been had we, say, exposed all individuals in our target population to a particular level of the exposure. When applying this framework to any analysis (regardless of
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