In population based genetic association studies, confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Propensity scores are often used to address confounding in observational...

Curve Registration is a technique for aligning a set of curves whose time scale is observed subject to random error. In this dissertation, a general approach to Curve Registration for longitudinal and functional data, in the possible presence of informative dropout and time-varying treatments,...

Two-stage predictor substitution (2SPS) and the two-stage residual inclusion (2SRI) are two approaches to instrumental variable (IV) analysis. While 2SPS and 2SRI with linear models are well-studied methods of causal inference, the properties of 2SPS and 2SRI for logistic binary outcomes have...

In causal inference for longitudinal data, standard methods usually assume that the underlying processes are discrete time processes, and that the observational time points are the time points when the processes change values. The identification of these standard models often relies on the...

In causal inference for longitudinal data, standard methods usually assume that the underlying processes are discrete time processes, and that the observational time points are the time points when the processes change values. The identification of these standard models often relies on the...

Two-stage predictor substitution (2SPS) and the two-stage residual inclusion (2SRI) are two approaches to instrumental variable (IV) analysis. While 2SPS and 2SRI with linear models are well-studied methods of causal inference, the properties of 2SPS and 2SRI for logistic binary outcomes have...

In randomized clinical trials where the effects of post-randomization factors are of interest, the standard regression analyses are biased due to unmeasured confounding. The instrumental variables (IV; Angrist et al., 1996) and G-estimation procedures under structural nested mean models (SNMMs;...

With the development of modern technology, tremendous amount of data can be collected in biomedical experiments. These data can arise as curves or groups of time series, therefore it is natural to use a curve or a time series as the basic unit in the data analysis. In this dissertation, we...

This research is concerned with estimating the relationship between the occurrence of metastases and the size of primary cancers. We are interested in finding the distribution functions of tumor sizes at the points of nodal metastatic and distant metastatic transitions. Understanding the...

Variable selection is a difficult problem in statistical model building. Identification of cost efficient diagnostic factors is very important to health researchers, but most variable selection methods do not take into account the cost of collecting data for the predictors. The trade off between...