Penalized variable selection for cause-specific hazard frailty models with clustered competing-risks data.

Competing risks data usually arise when an occurrence of an event precludes other types of events from being observed. Such data are often encountered in a clustered clinical study such as a multi-center clinical trial. For the clustered competing-risks data which are correlated within a cluster, competing-risks models allowing for frailty terms have been recently studied.

Local influence diagnostics for hierarchical finite-mixture random-effects models.

The main objective of this paper is to evaluate the influence of individual subjects exerted on a random-effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole.

Local influence diagnostics for hierarchical count data models with overdispersion and excess zeros.

We consider models for hierarchical count data, subject to overdispersion and/or excess zeros. Molenberghs et al. () and Molenberghs et al.

Elective reconstruction of the ascending aorta for aneurysmal disease restores normal life expectancy. An analysis of risk factors for early and late mortality.

Objective: We investigated the survival of patients who had undergone elective reconstruction of the ascending aorta for degenerative aneurysms. The long-term survival was compared to an age- and sex-matched case-control population. An analysis of risk factors, influencing survival was made.