Marginal mean models of temporal processes in event time data analysis are gaining more
attention for their milder assumptions than the traditional intensity models. Recent work on …

We consider a special type of recurrent event data, termed “recurrent episode” data, arising
in episodic illness studies. When an event occurs, it lasts for a random length of time. A …

This paper deals with analysis of data from longitudinal studies where the rate of a recurrent
event characterizing morbidity is the primary criterion for treatment evaluation. We consider …

In cancer research, interest frequently centers on factors influencing a latent event that must
precede a terminal event. In practice it is often impossible to observe the latent event …

Time-to-event models are a popular tool to analyse data where the outcome variable is the
time to the occurrence of a specific event of interest. Here, we focus on the analysis of time …

We propose a functional random effect time‐varying coefficient model to establish the
dynamic relationship between the response and predictor variables in longitudinal data …

Event duration and prevalence are important features for assessing outcomes of medical
treatment. Although semi-parametric approaches have been well developed for analysis of …

In this dissertation, an Expectation-Maximization (EM) algorithm is developed and
implemented to obtain maximum likelihood estimates of the parameters and the associated …

In functional data analysis for longitudinal data, the observation process is typically assumed
to be noninformative, which is often violated in real applications. Thus, methods that fail to …

In observational studies, the time origin of interest for time-to-event analysis is often
unknown, such as the time of disease onset. Existing approaches to estimating the time …