Current status data arise in studies where the target measurement is the time of occurrence
of some event, but observations are limited to indicators of whether or not the event has …

Current status data arise when the exact timing of an event is unobserved, and it is only
known at a given point in time whether or not the event has occurred. Recently Lin et …

We consider methods for the analysis of discrete-time recurrent event data, when interest is
mainly in prediction. The Aalen additive model provides an extremely simple and effective …

The semiparametric maximum likelihood estimation is considered in a three-state duration
dependent Markov process when times of the intermediate transition (eg, onset of a disease) …

The nonparametric maximum likelihood estimator for current-status data has been known for
at least 40 years, but only recently have the mathematical-statistical properties been …

We describe a method for extending smooth nonparametric modeling methods to time-to-
event data where the event may be known only to lie within a window of time. Maximum …

This paper considers nonparametric estimation of age-and time-specific trends in disease
incidence using serial prevalence data collected from multiple cross-sectional samples of a …

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 …

In biostatistical applications interest often focuses on the estimation of the distribution of time
between two consecutive events. If the initial event time is observed and the subsequent …

Publisher Summary This chapter describes non-and semiparametric methods of analyzing
recurrent event data. It focuses on the functions that are modeled in the analysis of recurrent …