Accounting for Competing Events when Evaluating Long-Term Outcomes in Survivors of Critical Illness

The clinical trajectory of survivors of critical illness following hospital discharge can be complex and highly unpredictable. Assessing long-term outcomes after critical illness can be challenging due to possible competing events such as all-cause death during follow-up (which preclude the occurrence of an event of particular interest). In this perspective, we explore challenges and methodological implications of competing events during the assessment of long-term outcomes in survivors of critical illness. In the absence of competing events, researchers evaluating long-term outcomes commonly use the Kaplan Meier method and the Cox proportional hazards model to analyze time to event (survival) data. However, traditional analytical and modelling techniques can yield biased estimates in the presence of competing events. We present different estimands of interest, and the use of different analytical approaches, including changes to the outcome of interest, Fine and Gray regression models, cause-specific Cox proportional hazards models, and g-methods (such as inverse probability weighting). Finally, we provide code, and a simulated dataset, to exemplify the application of the different analytical strategies, in addition to overall reporting recommendations.