B1123
Title: A general wild bootstrap scheme for counting process-based statistics with application to Fine-Gray models
Authors: Dennis Dobler - TU Dortmund (Germany) [presenting]
Mathisca de Gunst - Vrije Universiteit Amsterdam (Netherlands)
Marina Tiana Dietrich - Vrije Universiteit Amsterdam (Netherlands)
Abstract: The wild bootstrap is a popular resampling method in the context of time-to-event data analyses. Previous works established the large sample properties of it for applications to different estimators and test statistics. It can be used to justify the accuracy of inference procedures such as hypothesis tests or time-simultaneous confidence bands. A general framework is developed in which the large sample properties are established in a unified way by using martingale structures. The framework includes most of the well-known non- and semiparametric statistical methods in time-to-event analysis and parametric approaches. The Fine-Gray proportional sub-hazards model exemplifies the theory for inference on cumulative incidence functions given the covariates. The model falls within the framework if the data are censor-complete. However, not all censoring times are known in most real-life applications. Hence, the wild bootstrap is additionally combined with a multiple imputation of the required yet unknown censoring times. Simulation results are shown and an application to a data set about hospital-acquired infections is illustrated.
