On restricted optimal treatment regime estimation for competing risks data

Parametric Estimation in a Recurrent Competing Risks Model

A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the compet- ing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. ...

Estimation of Weibull Parameters for Grouped Data with Competing Risks

When experimental units, on a cross-sectional or a longitudinal study, are followed up periodically, the event(s) of interest may only be known to have occurred between two dates of follow-up. In this case data are usually presented in form of counts of events in each of the follow-up intervals (grouped data). Furthermore, in studies of survival, subjects may be at risk of failure due to more t...

Nonparametric Estimation for Current Status Data with Competing Risks

Nonparametric estimation with left truncated semi-competing risks data

SUMMARY Cause-specific hazard and cumulative incidence function are of practical importance in competing risks studies. Inferential procedures for these quantities are well developed and can be applied to semi-competing risks data, where a terminating event censors a non-terminating event, after coercing the data into the competing risks format. Complications arise when there is left truncation...

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...