We propose a new procedure for estimating the survival function of a time-to-event random variable under arbitrary patterns of censoring. Under mild smoothness assumptions, this procedure allows a unified approach to handling different kinds of censoring, while in many cases increasing...

A variety of complications arise when imperfect measurements, W, are observed in place of a true variable of interest, X. In the context of linear and non-linear regression models where X is a covariate, regression parameter estimators obtained when W is substituted for X may be substantially...

The accelerated failure time (AFT) model is a popular model for time-to-event data. It provides a useful alternative when the proportional hazards assumption is in question and it provides an intuitive linear regression interpretation where the logarithm of the survival time is regressed on the...

There are two topics in this dissertation. The first topic is 'Smoothing Parameter Selection in Nonparametric Generalized Linear Models via Sixth-order Laplace Approximation' and the second topic is 'Smoothing Spline-based Score Tests for Proportional Hazards Models'.We present a new approach...

In many studies, a primary endpoint and longitudinal measures of a continuous response are collected for each participant along with other covariates, and the association between the primary endpoint and features of the longitudinal profiles is of interest. One challenge is that the features of...

After treatment is found to be effective in a clinical study, attention often focuses on the effect of treatment duration on outcome. Such an analysis facilitates recommendations on the most beneficial treatment duration. In many studies, treatment duration is left to the discretion of the...

Model selection is important for longitudinal data analysis. But up to date little work has been done on variable selection for generalized linear mixed models (GLMM). In this paper we propose and study a class of variable selection methods. Full likelihood (FL) approach is proposed for...

Conventionally, values of nuisance parameters given in a statistical design are often erroneous, thus may result in overpowering or underpowering a test using traditional sample size calculations. In this thesis, we propose to use Fisher Information data monitoring in group sequential studies to...

Parametric estimation is complicated when data are measured with error. The problem of regression modeling when one or more covariates are measured with error is considered in this paper. It is often the case that, evaluated at the observed error-prone data, the unbiased true-data estimating...