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...

In this paper we model competing risks, default and early settlement events, in the presence of long term survivors compare survival logistic methodologies. Cause specific Cox regression models were fitted adjustments made to accommodate a proportion survivors. Methodologies compared using ROC curves area under curves. The results show that methods perform better than when modelling lifetime da...

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...

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...

Joint analysis of longitudinal measurements and survival data has received much attention in recent years. However, previous work has primarily focused on a single failure type for the event time. In this paper we consider joint modelling of repeated measurements and competing risks failure time data to allow for more than one distinct failure type in the survival endpoint which occurs frequent...

We provide new conditions for identification in accelerated failure time competing risks models. In our model, we specify unknown regression functions and the joint survivor function of latent disturbance terms nonparametrically. We show that the model can be identified with covariates that are independent of latent errors, provided that certain rank conditions are satisfied. We present a simpl...