The Use and Interpretation of Competing Risks Regression Models

The use and interpretation of competing risks regression models.

PURPOSE Competing risks observations, in which patients are subject to a number of potential failure events, are a feature of most clinical cancer studies. With competing risks, several modeling approaches are available to evaluate the relationship of covariates to cause-specific failures. We discuss the use and interpretation of commonly used competing risks regression models. EXPERIMENTAL D...

Classical Regression Models for Competing Risks

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Survival Analysis and Regression Models in the Presence of Competing and Semi-competing Risks

Semiparametric analysis of mixture regression models with competing risks data.

In the analysis of competing risks data, cumulative incidence function is a useful summary of the overall crude risk for a failure type of interest. Mixture regression modeling has served as a natural approach to performing covariate analysis based on this quantity. However, existing mixture regression methods with competing risks data either impose parametric assumptions on the conditional ris...

Competing risks and multistate models.

Complex clinical endpoints are present in studies in cancer. Especially in studies on hematopoietic stem-cell transplantation (HSCT), various risks exist after HSCT. Patients can experience acute and chronic graft versus host disease (GVHD) or need to undergo immunosuppressive therapy (IST), a relapse can occur, or patients can die after relapse or without former relapse (nonrelapse mortality, ...