Penalized Regression with Ordinal Predictors

Ordered categorial predictors are a common case in regression modeling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In this article penalized regression techniques are proposed. Based on dummy coding two types of penalization are explicitly developed; the first imposes a difference penalty, the second is a ridge type refitting procedure. A Bayesian motivation as well as alternative ways of derivation are provided. Simulation studies and real world data serve for illustration and to compare the approach to methods often seen in practice, namely linear regression on the group labels and pure dummy coding. The proposed regression techniques turn out to be highly competitive. On the basis of GLMs the concept is generalized to the case of non-normal outcomes by performing penalized likelihood estimation. The paper is a preprint of an article published in the International Statistical Review. Please use the journal version for citation

Concentrations of Pro-Inflammatory Cytokines Are Not Associated with Senescence Marker p16INK4a or Predictive of Intracellular Emtricitabine/Tenofovir Metabolite and Endogenous Nucleotide Exposures in Adults with HIV Infection

As the HIV-infected population ages, the role of cellular senescence and inflammation on co-morbid conditions and pharmacotherapy is increasingly of interest. p16INK4a expression, a marker for aging and senescence in T-cells, is associated with lower intracellular concentrations of endogenous nucleotides (EN) and nucleos(t)ide reverse transcriptase inhibitors (NRTIs). This study expands on these findings by determining whether inflammation is contributing to the association of p16INK4a expression with intracellular metabolite (IM) exposure and endogenous nucleotide concentrations

Application and Extension of Weighted Quantile Sum Regression for the Development of a Clinical Risk Prediction Tool

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient’s health and the overall function of different organs. In this dissertation, we develop and validate a weighted composite index that aggregates the information from a variety of health biomarkers covering multiple organ systems. The index can be used for predicting all-cause mortality and could also be used as a holistic measure of overall physiological health status. We refer to it as the Health Status Metric (HSM). Validation analysis shows that the HSM is predictive of long-term mortality risk and exhibits a robust association with concurrent chronic conditions, recent hospital utilization, and self-rated health. We develop the HSM using Weighted Quantile Sum (WQS) regression (Gennings et al., 2013; Carrico, 2013), a novel penalized regression technique that imposes nonnegativity and unit-sum constraints on the coefficients used to weight index components. In this dissertation, we develop a number of extensions to the WQS regression technique and apply them to the construction of the HSM. We introduce a new guided approach for the standardization of index components which accounts for potential nonlinear relationships with the outcome of interest. An extended version of the WQS that accommodates interaction effects among index components is also developed and implemented. In addition, we demonstrate that ensemble learning methods borrowed from the field of machine learning can be used to improve the predictive power of the WQS index. Specifically, we show that the use of techniques such as weighted bagging, the random subspace method and stacked generalization in conjunction with the WQS model can produce an index with substantially enhanced predictive accuracy. Finally, practical applications of the HSM are explored. A comparative study is performed to evaluate the feasibility and effectiveness of a number of ‘real-time’ imputation strategies in potential software applications for computing the HSM. In addition, the efficacy of the HSM as a predictor of hospital readmission is assessed in a cohort of emergency department patients

Population Effect Model Identifies Gene Expression Predictors of Survival Outcomes in Lung Adenocarcinoma for Both Caucasian and Asian Patients

Background: We analyzed and integrated transcriptome data from two large studies of lung adenocarcinomas on distinct populations. Our goal was to investigate the variable gene expression alterations between paired tumor-normal tissues and prospectively identify those alterations that can reliably predict lung disease related outcomes across populations. Methods: We developed a mixed model that combined the paired tumor-normal RNA-seq from two populations. Alterations in gene expression common to both populations were detected and validated in two independent DNA microarray datasets. A 10-gene prognosis signature was developed through a l1 penalized regression approach and its prognostic value was evaluated in a third independent microarray cohort. Results: Deregulation of apoptosis pathways and increased expression of cell cycle pathways were identified in tumors of both Caucasian and Asian lung adenocarcinoma patients. We demonstrate that a 10-gene biomarker panel can predict prognosis of lung adenocarcinoma in both Caucasians and Asians. Compared to low risk groups, high risk groups showed significantly shorter overall survival time (Caucasian patients data: HR = 3.63, p-value = 0.007; Asian patients data: HR = 3.25, p-value = 0.001). Conclusions: This study uses a statistical framework to detect DEGs between paired tumor and normal tissues that considers variances among patients and ethnicities, which will aid in understanding the common genes and signalling pathways with the largest effect sizes in ethnically diverse cohorts. We propose multifunctional markers for distinguishing tumor from normal tissue and prognosis for both populations studied

Using Penalized Regression to Uncover Peer Effects in the Spatial Autoregressive Model

We propose a technique for estimating the spatial weights matrix (SWM) of the spatial autoregressive model (SAR) using the least absolute shrinkage and selection operator (Lasso), first proposed by Tibshirani (1996). The SWM is typically assumed a priori as a known matrix of correlations among spatially correlated data, as it cannot be estimated using standard techniques due to overfitting. However, we use the Lasso to discover the most prominent spatial coefficients in the SWM and estimate them using feasible generalized least squares, while setting the relatively unimportant effects to zero. Furthermore, the Lasso solutions are optimized to minimize mean squared error over a grid of possible spatial lag parameters. Finally, we use the LARS-Lasso algorithm to compute an illustrative example of the technique