Competing risks analysis of time-to-event data for cardiovascular surgeons

Objectives: The purpose of this article is to provide thoracic and cardiovascular surgeons with a practical step-by-step strategy to use in collaboration with a biostatistician for implementation of competing risks analysis when analyzing time-to-event data. Patients may have an outside event that precludes the event of interest. Traditional time-to-event analysis incorrectly assumes noninformative censoring in this scenario, which will lead to invalid results and conclusions. Methods: The steps are (1) to determine whether competing risks analysis is needed, (2) to perform a nonparametric analysis, (3) to perform a model-based analysis, (4) to interpret the results, and (5) to compare to traditional survival analysis methods. We apply our approach to a hypothetical cardiovascular surgery example in determining the hazard of mortality after the stage 3 Fontan operation associated with prematurity among patients with hypoplastic left heart syndrome who had successful completion of Norwood stage 1 while incorporating mortality during the stage 2 bidirectional Glenn procedure as a competing risk. We apply nonparametric, semiparametric, and parametric methods. Results: Although Cox regression establishes prematurity as a significant risk factor of mortality after stage 3 (hazard ratio, 1.26; 95% confidence interval, 1.06-1.50; P =.009), the competing risks analysis with the Fine-Gray model accounting for mortality after stage 2 determines that prematurity is not a significant predictor (hazard ratio, 1.07; 95% confidence interval, 0.90-1.27; P = .467). Conclusions: This article provides a practical step-by-step approach for making competing risks more accessible for cardiac surgeons collaborating with a biostatistician in analyzing and interpreting time-to- event data.