Methods for generating paired competing risks data

Competing Risks Methods

Competing risks regression for clustered data.

A population average regression model is proposed to assess the marginal effects of covariates on the cumulative incidence function when there is dependence across individuals within a cluster in the competing risks setting. This method extends the Fine-Gray proportional hazards model for the subdistribution to situations, where individuals within a cluster may be correlated due to unobserved s...

Semi-Competing Risks Data Analysis

Hospital readmission is a key marker of quality of healthcare; it has been used to investigate variation in quality among patients in a broad range of clinical contexts and has become an important policy measure. Notwithstanding its widespread use, however, readmission remains controversial as a measure of quality. Among the concerns raised, whether and how patient deaths are handled in the ana...

Simulating competing risks data in survival analysis.

Competing risks analysis considers time-to-first-event ('survival time') and the event type ('cause'), possibly subject to right-censoring. The cause-, i.e. event-specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized latent failure time model. Cause-specific hazard-driven simulation appears to be the exception; if done, ...

Cumulative incidence in competing risks data and competing risks regression analysis.

Competing risks occur commonly in medical research. For example, both treatment-related mortality and disease recurrence are important outcomes of interest and well-known competing risks in cancer research. In the analysis of competing risks data, methods of standard survival analysis such as the Kaplan-Meier method for estimation of cumulative incidence, the log-rank test for comparison of cum...