The need to develop treatments and/or programs specific to a disease requires the analysis of outcomes to be specific to that disease. Such endpoints as heart failure, death due to a specific disease, or control of local disease in cancer may become impossible to observe due to a prior occurrence of a different type of event (such as death from another cause). The event which hinders or changes...

In studies with survival or time-to-event outcomes, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Specialized statistical methods must be used to analyze survival data in the presence of competing risks. We conducted a review of randomized controlled trials with survival outcomes that were published in high-impact general medical journa...

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, ...

Some fully general parametric competing risks models, which allow the underlying (possibly dependent) lifetimes to be modeled as a function of an arbitrary number of covariables, are formulated, and their resulting likelihood function is derived. Under carefully stated regularity conditions, large sample tests of hypotheses concerning the model parameters are obtained for the single point trunc...

The classical approach to the modeling of discrete time competing risks consists of fitting multinomial logit models where parameters are estimated using maximum likelihood theory. Since the effects of covariates are specific to the target events, the resulting models contain a large number of parameters, even if there are only few predictor variables. Due to the large number of parameters clas...

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...

The competing risks too arise when one type of event may affect the probability of occurrence of other events. Some authors have made important contributions in this area of research, multistate multivariate models have been proposed. Varieties of record are frequently encountered in areas as medicine, engineering, sociology, biology, social science, among others. Classical survival analysis mo...

New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to examine survival times for nosocomial pneumonia and mortality. Their model was able to incorporate...

Absolutely continuous bivariate exponential (ACBVE) models have been widely used in the analysis of competing risks data involving two risk components. For such an analysis, frequentist approach often runs into difficulty due to a likelihood containing some nonidentifiable parameters. With an end to overcome this nonindentifiability, we consider Bayesian procedures. Utilization of informative p...