We propose a semiparametric additive rate model for modelling recurrent events in the
presence of a terminal event. The dependence between recurrent events and terminal event …

Recurrent event data are often encountered in biomedical research, for example, recurrent
infections or recurrent hospitalizations for patients after renal transplant. In many studies …

Recurrent event data are frequently encountered in longitudinal follow-up studies. In
statistical literature, noninformative censoring is typically assumed when statistical methods …

In this article we study a class of semiparametric transformation models with random effects
for the intensity function of the counting process. These models provide considerable …

A major complication in the analysis of recurrent event data from medical studies is the
presence of death. We consider the marginal mean function for the cumulative number of …

Recurrent event data often arise in biomedical studies, with examples including
hospitalizations, infections, and treatment failures. In observational studies, it is often of …

Recurrent event data are commonly encountered in clinical and epidemiological studies. A
major complication arises when recurrent events are terminated by death. To assess the …

In many clinical studies involving event history analysis, the event of interest is non-fatal and
may occur more than once for each subject. Models based on the theory of counting …

Recurrent event data occur in many clinical and observational studies, and in these
situations, there may exist a terminal event such as death that is related to the recurrent …

Recurrent events are frequently observed in biomedical studies, and often more than one
type of event is of interest. Follow-up time may be censored due to loss to follow-up or …