Biostatistics Commons^™

Augmenting Program Data With Secondary Data Sources To Improve The Quality Of Existing Statistics : Four Examples From The New York State Department Of Health Hospital-Acquired Infection Reporting Program, Valerie Benson Haley

Legacy Theses & Dissertations (2009 - 2024)

The objective of this dissertation is to illustrate the novel application of methods that can be used to improve the accuracy of hospital-acquired infection (HAI) rates. Public reporting of HAI rates is relatively new. New York was one of the first states to mandate reporting in acute care hospitals (2007), followed by national pay-for-reporting in 2011, and national value-based purchasing in 2013. Given the financial ramifications of public reporting, it is critical that the data are validated and adjusted for differences in underlying risk among patient populations so that hospital performance can be fairly compared. However, limited information on the …

Flexible Variable And Model Selection With Ordinal Categorical Responses And Multiple Covariates, Wan-Hsiang Hsu

Legacy Theses & Dissertations (2009 - 2024)

Ordered categorical responses are common in many applied studies; moreover, with the rapid growth of computational power and technologies, ultrahigh dimensional data becomes very widespread. For example, there could be ten thousands of dimensions in a gene expression data and of interest is to classify the disease stage with specific genes and predict a clinical prognosis by using these specific genes. However, there are some unique challenges, including (1) the curse of dimensionality and (2) the modeling strategy for allowing dynamic covariate effects. Thus, variable selection for mining ultrahigh dimensional data and flexible modeling strategy for allowing dynamic covariate effects …

Data Mining And Pattern Discovery Using Exploratory And Visualization Methods For Large Multidimensional Datasets, Hsin-Fang Li

Theses and Dissertations--Epidemiology and Biostatistics

Oral health problems have been a major public health concern profoundly affecting people’s general health and quality of life. Given that oral health data is composed of several measurable dimensions including clinical measurements, socio-behavioral factors, genetic predispositions, self-reported assessments, and quality of life measures, strategies for analyzing multidimensional data are neither computationally straightforward nor efficient. Researchers face major challenges to identify tools that circumvent the processes of manually probing the data.

The purpose of this dissertation is to provide applications of the proposed methodology on oral health-related data that go beyond identifying risk factors from a single dimension, and to …

Models And Software Development For Interval-Censored Data, Chun Pan

Theses and Dissertations

Interval-censored time-to-event data occur naturally in studies of diseases where the symptoms are not directly observable, and periodic clinical examinations are required for detection. Due to the lack of well-established procedures, interval-censored data have been conventionally treated as right-censored data, however, this introduces bias at the first place. This dissertation focuses on methodological research and software development for interval-censored data. Specifically, it consists of three projects. The first project is to create an R package for regression analysis and survival curve estimation of interval-censored data based on several published papers by our research team. In the second project, a Bayesian …

Predictors Of Treatments Acceptable To Patients For Late-Life Depression, Gerald J. Jogerst, Shimin Zheng, Erik Vanderlip

ETSU Faculty Works

Objectives. Describe older patients’ perceptions about depression and characteristics associated with acceptance of treatments. Design. Cross-sectional study. Setting. Three primary care clinics in Iowa. Participants. Consecutive sample of 529 primary care patients. Measurements. Depression screening tool (a 9-item patient health questionnaire [PHQ-9]) and questionnaire including sociodemographic data, patient attitudes about depression, and acceptability of different treatments. Results. Mean age was 71.9 years (range 60–93 years), 314 (59%) female. Among the 529 participants, 93 (17.5%) had history of depression and 60 (11.3%) had PHQ-9 scores of 10 or greater. Participants believed depression is a disease for which they would use medication …

Screening For Elder Mistreatment Among Older Adults Seeking Legal Assistance Services, Sheryl M. Strasser, Megan Smith, Scott Weaver, Shimin Zheng, Yan Cao

ETSU Faculty Works

Introduction: The aging population is a rapidly growing demographic in the United States. Isolation, limited autonomy, and declining physical and mental health render many older adults vulnerable to elder mistreatment (EM). The purpose of this study was to assess the prevalence and correlates of EM among a sample of older adults using legal assistance services in Atlanta, Georgia.

Methods: Researchers administered surveys to consenting older adults (aged 60þ) in 5 metro Atlanta community centers that hosted legal assistance information sessions as part of the Elderly Legal Assistance Program. The surveys screened for risk factors and prevalence of EM risk using …

A New Method For The Comparison Of Survival Distributions, Jaymie Shanahan

Theses and Dissertations

The assessment of overall homogeneity of time-to-event curves is a key element in survival analysis in biomedical research. The currently commonly used testing methods, e.g. log-rank test, Wilcoxon test, and Kolmogorov-Smirnov test, may have a significant loss of statistical testing power under certain circumstances. In this thesis we replicate a testing method (Lin & Xu, 2009) that is robust for the comparison of the overall homogeneity of survival curves based on the absolute difference of the area under the survival curves using normal approximation by Greenwood's formula, and propose a new weight component to their test statistic. The weight component …

Advanced Methodology Developments In Mixture Cure Models, Chao Cai

Theses and Dissertations

Modern medical treatments have substantially improved cure rates for many chronic diseases and have generated increasing interest in appropriate statistical models to handle survival data with non-negligible cure fractions. The mixture cure models are designed to model such data set, which assume that studied population is a mixture of being cured and uncured. In this dissertation, I will develop two programs named smcure and NPHMC in R. The first program aims to facilitate estimating two popular mixture cure models: the proportional hazards (PH) mixture cure model and accelerated failure time (AFT) mixture cure model. The second program focuses on designing …

Uncontrolled Hypertension And Associated Factors In Hypertensive Patients At The Primary Healthcare Center Luis H. Moreno, Panama: A Feasibility Study, Roderick Ramon Chen Camano

USF Tampa Graduate Theses and Dissertations

Background: According to the World Health Organization (WHO), hypertension is a major risk factor for cardiovascular disease (CVD), renal impairment, peripheral vascular disease, and blindness. In Panama, a recent study estimated the prevalence of hypertension at 38.5% in the two main provinces of the country, with a rate of uncontrolled hypertension of 47.2%.

Objectives: The aims of this study were to assess the feasibility of the study design and to describe the characteristics of the hypertensive population and the physician's adherence to Panamanian antihypertensive protocols and their relationship with uncontrolled hypertension.

Methods: This is a cross-sectional study of adult hypertensive …

Multiple Calibrations In Integrative Data Analysis: A Simulation Study And Application To Multidimensional Family Therapy, Kristin Wynn Hall

USF Tampa Graduate Theses and Dissertations

A recent advancement in statistical methodology, Integrative Data Analyses (IDA Curran & Hussong, 2009) has led researchers to employ a calibration technique as to not violate an independence assumption. This technique uses a randomly selected, simplified correlational structured subset, or calibration, of a whole data set in a preliminary stage of analysis. However, a single calibration estimator suffers from instability, low precision and loss of power. To overcome this limitation, a multiple calibration (MC; Greenbaum et al., 2013; Wang et al., 2013) approach has been developed to produce better estimators, while still removing a level of dependency in the data …

A Latent Mixture Approach To Modeling Zero-Inflated Bivariate Ordinal Data, Rajendra Kadel

USF Tampa Graduate Theses and Dissertations

Multivariate ordinal response data, such as severity of pain, degree of disability, and satisfaction with a healthcare provider, are prevalent in many areas of research including public health, biomedical, and social science research. Ignoring the multivariate features of the response variables, that is, by not taking the correlation between the errors across models into account, may lead to substantially biased estimates and inference. In addition, such multivariate ordinal outcomes frequently exhibit a high percentage of zeros (zero inflation) at the lower end of the ordinal scales, as compared to what is expected under a multivariate ordinal distribution. Thus, zero inflation …

A Monte Carlo Approach To Change Point Detection In A Liver Transplant, Alexia Melissa Makris

USF Tampa Graduate Theses and Dissertations

Patient survival post liver transplant (LT) is important to both the patient and the center's accreditation, but over the years physicians have noticed that distant patients struggle with post LT care. I hypothesized that patient's distance from the transplant center had a detrimental effect on post LT survival. I suspected Hepatitis C (HCV) and Hepatocellular Carcinoma (HCC) patients would deteriorate due to their recurrent disease and there is a need for close monitoring post LT. From the current literature it was not clear if patients' distance from a transplant center affects outcomes post LT. Firozvi et al. (Firozvi AA, 2008) …

Age Dependent Analysis And Modeling Of Prostate Cancer Data, Nana Osei Mensa Bonsu

USF Tampa Graduate Theses and Dissertations

Growth rate of prostate cancer tumor is an important aspect of understanding the natural history of prostate cancer. Using real prostate cancer data from the SEER database with tumor size as a response variable, we have clustered the cancerous tumor sizes into age groups to enhance its analytical behavior. The rate of change of the response variable as a function of age is given for each cluster. Residual analysis attests to the quality of the analytical model and the subject estimates. In addition, we have identified the probability distribution that characterize the behavior of the response variable and proceeded with …

Optimal Spatial Prediction Using Ensemble Machine Learning, Molly M. Davies, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Spatial prediction is an important problem in many scientific disciplines. Super Learner is an ensemble prediction approach related to stacked generalization that uses cross-validation to search for the optimal predictor amongst all convex combinations of a heterogeneous candidate set. It has been applied to non-spatial data, where theoretical results demonstrate it will perform asymptotically at least as well as the best candidate under consideration. We review these optimality properties and discuss the assumptions required in order for them to hold for spatial prediction problems. We present results of a simulation study confirming Super Learner works well in practice under a …

Relating Nanoparticle Properties To Biological Outcomes In Exposure Escalation Experiments, Trina Patel, Cecile Low-Kam, Zhaoxia Ji, Haiyuan Zhang, Tian Xia, Andre E. Nel, Jeffrey I. Zinc, Donatello Telesca

COBRA Preprint Series

A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The first line of screening for potentially adverse outcomes often consists of exposure escalation experiments, involving the exposure of micro-organisms or cell lines to a battery of nanomaterials. We discuss a modeling strategy, that relates the outcome of an exposure escalation experiment to nanoparticle properties. Our approach makes use of a hierarchical decision process, where we jointly identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physico-chemical descriptors. The …

Evaluation Of The Survival Effect For Various Treatment Modalities Among Stage Ii And Iii Rectal Cancer Patients In California, 1994-2009, Myung Mi Cho

Loma Linda University Electronic Theses, Dissertations & Projects

Background: European trials evaluating the effect of preoperative (PreOP) versus postoperative chemoradiotherapy (PostOP CRT) found no survival benefit. However, the effect of a change from PostOP to PreOP CRT has not been evaluated in a population-based setting. We sought to evaluate multimodal treatment changes and overall survival for perioperative (PeriOP) CRT versus surgery alone and for PreOP versus PostOP CRT from 1994 through 2009 among patients receiving radical surgery for stage II and III rectal cancer (RC).

Patients and Methods: We conducted a nonconcurrent cohort study evaluating demographic predictors of multimodal therapy for stage II and III RC using …

Sensitivity Analysis For Causal Inference Under Unmeasured Confounding And Measurement Error Problems, Iván Díaz, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In this paper we present a sensitivity analysis for drawing inferences about parameters that are not estimable from observed data without additional assumptions. We present the methodology using two different examples: a causal parameter that is not identifiable due to violations of the randomization assumption, and a parameter that is not estimable in the nonparametric model due to measurement error. Existing methods for tackling these problems assume a parametric model for the type of violation to the identifiability assumption, and require the development of new estimators and inference for every new model. The method we present can be used in …

Computationally Efficient Confidence Intervals For Cross-Validated Area Under The Roc Curve Estimates, Erin Ledell, Maya L. Petersen, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In binary classification problems, the area under the ROC curve (AUC), is an effective means of measuring the performance of your model. Most often, cross-validation is also used, in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we must obtain an estimate for its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, calculating the cross-validated AUC on even a relatively small data set can still require a …

A National Model Built With Partial Least Squares And Universal Kriging And Bootstrap-Based Measurement Error Correction Techniques: An Application To The Multi-Ethnic Study Of Atherosclerosis, Silas Bergen, Lianne Sheppard, Paul D. Sampson, Sun-Young Kim, Mark Richards, Sverre Vedal, Joel Kaufman, Adam A. Szpiro

UW Biostatistics Working Paper Series

Studies estimating health effects of long-term air pollution exposure often use a two-stage approach, building exposure models to assign individual-level exposures which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error. To illustrate the importance of carefully accounting for exposure model characteristics in two-stage air pollution studies, we consider a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA). We present national spatial exposure models that use partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), …

An Analysis Of Risk Reduction Choices In Dcis Breast Cancer Patients, Lauren Soltesz

Statistics

The main focus of this paper was to evaluate possible demographic and clinical characteristics associated with a woman’s choice of breast conserving surgery (BCS), unilateral mastectomy (ULM), or bilateral risk reduction mastectomy (BRRM). The cohort consisted of patients presenting to the City of Hope National Medical Center with ductal carcinoma in situ breast cancer who elected to have cancer directed surgery (N=305). Analyses to examine associations of patient characteristics with type of surgery were conducted using a multinomial logistic regression. Results showed that older women were more likely to choose breast conserving surgery over bilateral risk reduction mastectomy than younger …