A competing risk event is any event that ensures that the outcome of interest cannot subsequently occur. For example, in a study where incident dementia is the primary outcome, death is a competing event because dementia cannot onset after an individual has died. When competing events are present, many possible definitions of a causal effect may be considered. Choosing a causal effect of practical interest requires understanding the interpretation of different counterfactual contrasts and the assumptions needed to identify those contrasts using the study data and subject matter knowledge. This workshop will introduce participants to a counterfactual framework for causal inference in the face of competing events. Participants will learn how to articulate and interpret different types of causal effects when competing events are present, and approaches to estimating them under transparent assumptions with the aid of causal diagrams. In part I, we consider counterfactual contrasts of popular parameters from the competing risks literature including cause-specific and subdistribution hazards, and cause-specific cumulative incidences and consider their relation to total and controlled direct effects from the mediation literature. In part II, we introduce the separable effects, new causal effect definitions that may be of particular clinical relevance in competing events settings. Each part will outline the theoretical concepts through examples and provide hands-on exercises in R.

June 14, 2022 8:30am – 12:30pm

July 15, 2022 10:00 – 2:00pm MT

• Jessica Young
• Mats Stensrud
• L. Paloma Rojas-Saunero

• R folder: Includes R scripts and data

  • Code