Bayesian variable selection in parametric and semiparametric high dimensional survival analysis

Title from PDF of title page (University of Missouri--Columbia, viewed on May 21, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisors: Dr. Sounak Chakraborty, Dr. (Tony) Jianguo SunVita."July, 2011"In this dissertation, we propose several Bayesian variable selection schemes for Bayesian parametric and semiparametric survival models for right-censored survival data. In the rst chapter we introduce a special shrinkage prior on the coeficients corresponding to the predictor variables. The shrinkage prior is obtained through a scale mixture representation of Normal and Gamma distributions. The likelihood function is constructed based on the Cox proportional hazards model framework, where the cumulative baseline hazard function is modeled a priori by a gamma process. In the second chapter we extend the idea of the shrinkage prior such that it can incorporate the existing grouping structure among the covariates. Our selected priors are similar to the elastic-net, group lasso, and fused lasso penalty. The proposed models are highly useful when we want to take into consideration the grouping structure. In the third chapter we propose a Bayesian variable selection method for high dimensional survival analysis in the context of parametric accelerated failure time (AFT) model. To identify subsets of relevant covariates the regression coe cients are assumed to follow the conditional Laplace distribution as in the first chapter. We used a data augmentation approach to impute the survival times of censored subjects.Includes bibliographical reference

Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer

Readmission following discharge from an initial hospitalization is a key marker of quality of health care in the United States. For the most part, readmission has been used to study quality of care for patients with acute health conditions, such as pneumonia and heart failure, with analyses typically based on a logistic-Normal generalized linear mixed model. Applying this model to the study readmission among patients with increasingly prevalent advanced health conditions such as pancreatic cancer is problematic, however, because it ignores death as a competing risk. A more appropriate analysis is to imbed such studies within the semi-competing risks framework. To our knowledge, however, no comprehensive statistical methods have been developed for cluster-correlated semi-competing risks data. In this paper we propose a novel hierarchical modeling framework for the analysis of cluster-correlated semi-competing risks data. The framework permits parametric or non-parametric specifications for a range of model components, including baseline hazard functions and distributions for key random effects, giving analysts substantial flexibility as they consider their own analyses. Estimation and inference is performed within the Bayesian paradigm since it facilitates the straightforward characterization of (posterior) uncertainty for all model parameters including hospital-specific random effects. The proposed framework is used to study the risk of readmission among 5,298 Medicare beneficiaries diagnosed with pancreatic cancer at 112 hospitals in the six New England states between 2000-2009, specifically to investigate the role of patient-level risk factors and to characterize variation in risk across hospitals that is not explained by differences in patient case-mix

Group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data

An important task in health research is to characterize time-to-event outcomes such as disease onset or mortality in terms of a potentially high-dimensional set of risk factors. For example, prospective cohort studies of Alzheimer's disease typically enroll older adults for observation over several decades to assess the long-term impact of genetic and other factors on cognitive decline and mortality. The accelerated failure time model is particularly well-suited to such studies, structuring covariate effects as `horizontal' changes to the survival quantiles that conceptually reflect shifts in the outcome distribution due to lifelong exposures. However, this modeling task is complicated by the enrollment of adults at differing ages, and intermittent followup visits leading to interval censored outcome information. Moreover, genetic and clinical risk factors are not only high-dimensional, but characterized by underlying grouping structure, such as by function or gene location. Such grouped high-dimensional covariates require shrinkage methods that directly acknowledge this structure to facilitate variable selection and estimation. In this paper, we address these considerations directly by proposing a Bayesian accelerated failure time model with a group-structured lasso penalty, designed for left-truncated and interval-censored time-to-event data. We develop a custom Markov chain Monte Carlo sampler for efficient estimation, and investigate the impact of various methods of penalty tuning and thresholding for variable selection. We present a simulation study examining the performance of this method relative to models with an ordinary lasso penalty, and apply the proposed method to identify groups of predictive genetic and clinical risk factors for Alzheimer's disease in the Religious Orders Study and Memory and Aging Project (ROSMAP) prospective cohort studies of AD and dementia

Triclinic Na3.12Co2.44(P2O7)(2) as a High Redox Potential Cathode Material for Na-Ion Batteries

Two types of sodium cobalt pyrophosphates, triclinic Na3.12Co2.44(P2O7)(2) and orthorhombic Na2CoP2O7, are compared as high-voltage cathode materials for Na-ion batteries. Na2CoP2O7 shows no electrochemical activity, delivering negligible capacity. In contrast, Na3.12Co2.44(P2O7)(2) exhibits good electrochemical performance, such as high redox potential at ca. 4.3 V (vs. Na/Na+) and stable capacity retention over 50 cycles, although Na3.12Co2.44(P2O7)(2) delivered approximately 40 mA h g(-1). This is attributed to the fact that Na2CoP2O7 (similar to 3.1 angstrom) has smaller diffusion channel size than Na3.12Co2.44(P2O7)(2) (similar to 4.2 angstrom). Moreover, the electrochemical performance of Na3.12Co2.44(P2O7)(2) is examined using Na cells and Li cells. The overpotential of Na cells is smaller than that of Li cells. This is due to the fact that Na3.12Co2.44(P2O7)(2) has a smaller charge transfer resistance and higher diffusivity for Na+ ions than Li+ ions. This implies that the large channel size of Na3.12Co2.44(P2O7)(2) is more appropriate for Na+ ions than Li+ ions. Therefore, Na3.12Co2.44(P2O7)(2) is considered a promising high-voltage cathode material for Na-ion batteries, if new electrolytes, which are stable above 4.5 V vs. Na/Na+, are introduced.