Gray's extension of Cox's proportional hazards (PH) model for right-censored survival data allows for a departure from the PH assumption via introduction of time-varying regression coefficients (TVC) using penalized splines. Gray's work focused on estimation, inference and residual analyses, but...

Parametric regression models are widely used in public health sciences. This dissertation is concerned with statistical inference under such models with some covariates missing at random. Under natural conditions, parameters remain identifiable from the observed (reduced) data. If the always...

In many maintenance treatment trials, patients are first enrolled into an open treatmentbefore they are randomized into treatment groups. During this period, patients are followedover time with their responses measured longitudinally. This design is very common intoday's public health studies of...

One difficulty in regression analysis for longitudinal data is that the outcomes are oftenmissing in a non-ignorable way (Little & Rubin, 1987). Likelihood based approaches todeal with non-ignorable missing outcomes can be divided into selection models and patternmixture models based on the way...

Missing problem is very common in today's public health studies because of responses measured longitudinally. In this dissertation we proposed two latent variable models for longitudinal data with informative missingness. In the first approach, a latent variable model is developed for the...

Survival analysis has been used to estimate underlying survival or failure probabilities and to estimate the effects of covariates on survival times. The Cox proportional hazards regression model is the most commonly used approach. However, in practical situations, the assumption of proportional...

Medication non-adherence impacts public health by impeding the evaluation of medication efficacy, decreasing improvement and/or increasing morbidity in patients, while increasing health care costs. As a result, intervention studies are designed to improve adherence rates. Medication adherence is...

The selection of variables used to predict a time to event outcome is a common and important issue when analyzing survival data. This is an essential step in accurately assessing risk factors in medical and public health studies. Ignoring an important variable in a regression model may result in...

This dissertation addresses regression models with missing covariate data. These methods are shown to be significant to public health research since they enable researchers to use a wider spectrum of data. Unbiased estimating equations are the focus of this dissertation, predominantly...

In a longitudinal study of biomarker data collected during a hospital stay, observations may be missing due to administrative reasons, the death of the subject or the subject's discharge from the hospital, resulting in non-ignorable missing data. Standard likelihood-based methods for the...