A causal framework for classical statistical estimands in failure‐time settings with competing events. (27th January 2020)

• Record Type: Journal Article
• Authors: Young, Jessica G.; Stensrud, Mats J.; Tchetgen Tchetgen, Eric J.; Hernán, Miguel A.
• Is Part Of: Statistics in medicine. Volume 39:Number 8(2020)
• Page Start: 1199
• Page End: 1236
• Publication Date: 2020-01-27
• Subjects: causal inference -- competing risks -- g‐formula -- inverse probability weighting -- longitudinal data -- survival analysis; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8471
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : In failure‐time settings, a competing 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 by 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.