The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach
provides an innovative look at methods for the analysis of correlated failure times. The focus …

This paper reviews contributions to multivariate survival analysis using random effect
models. We focus on three modeling approaches: the proportional hazards frailty model, the …

Multivariate failure time data are commonly encountered in scientific investigations because
each study subject may experience multiple events or because there exists clustering of …

Statistical estimation and inference for marginal hazard models with varying coefficients for
multivariate failure time data are important subjects in survival analysis. A local pseudo …

Multivariate failure time data arise in various forms including recurrent event data when
individuals are followed to observe the sequence of occurrences of a certain type of event; …

In many biomedical studies, covariates are subject to measurement error. Although it is well
known that the regression coefficients estimators can be substantially biased if the …

In the analysis of multivariate failure‐time data, the effect of a treatment or an exposure on
the hazard of each failure type is sometimes evaluated using only the information on the first …

Multivariate failure time data are frequently encountered in longitudinal studies when
subjects may experience several events or when there is a grouping of individuals into a …

Cox's seminal 1972 paper on regression methods for possibly censored failure time data
popularized the use of time to an event as a primary response in prospective studies. But …

There has been much research on analysing multivariate failure times, but little that has
accommodated failures that arise in the presence of a competing failure process. This paper …