Tree-structured survival analysis (TSSA) is used to recursively detect covariate values that best divide the sample into subsequent subsets with respect to a time to event outcome. The result is a set of empirical classification groups, each of which identifies individuals with more homogeneous...

Adaptive treatment regime is a set of rules that governs the assignment of time-varying treatment based on observed covariates and intermediate response. Treatment choices are made sequentially as patients make transition from one health state to another. Specifically, in two stage randomization...

In the analysis of time-to-event data, the median residual life (MERL) function has been promoted by many researchers as a practically relevant summary of the residual life distribution. Formally the MERL function at a time point is defined as the median of the remaining lifetimes among...

Frailties models, an extension of the proportional hazards model, are used to model clustered survival data. In some situations there may be competing risks within a cluster. When this happens the basic frailty model is no longer appropriate. Depending on the purpose of the analysis, either the...

Longitudinal censoring is a common artifact when evaluating biomarkers and an obstacle to overcome when jointly investigating the longitudinal nature of the data and the impact on the survival prognoses of a study population. To fully appreciate the complexity of this scenario one has to devise...

The joint effect of several variables is a prevailing statistical concept in biology. The public health importance of developing methods to better assess joint effects is evident when studying gene combinations that function together to produce a disease phenotype, or biomarker pairs that...

Many research areas require multiple outcomes. For example, neuropsychological hypotheses may not be testable using a single measure. Similarly, genetic researchers frequently examine multiple markers across the genome. Examining multiple hypotheses requires the use of multiple testing...

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...

Objective: To investigate the relationship between the area under the Receiver Operating Characteristic curve (AUC) of the propensity model for exposure and the accuracy of the estimated effect of the exposure on the outcome of interest.Methods: A Monte Carlo simulation study was performed where...

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...