Statistics and Probability Commons^™

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 …

The Morbidity & Mortality Of Prevalent Heart Failure, Jennifer Kwon

Loma Linda University Electronic Theses, Dissertations & Projects

The first study population included 292 unselected consecutive patients from the LLUMC heart failure clinic who were enrolled in the study from January to July 2006 and were followed up through the end of December 2010. The treatment policy at the clinic was to uptitrate dosages of beta-adrenergic blockade (β-blockers), angiotensin-converting-enzyme inhibitors (ACEi) and angiotensin II receptor blockers (ARB) to the most tolerable levels in order to reach target dosages, as recommended by the Heart Failure Society of America (HFSA). Patients were classified into systolic heart failure (ejection fraction (EF) < 40%) or diastolic heart failure (EF≥40%). All dosages of β-blockers, ACEi and ARB were extracted through chart reviews and were used as the main predictors of the patients' survival. Results from analyses showed that reaching target dosages of β-blockers and ACEi/ARB may increase survival when compared to not reaching target among the systolic HF population (HR_{β_biockers}= 0.64, 95% CI 0.26-1.56 and HR_ACEi/ARB=0.50, …

Limited Sampling Estimates Of Epigallocatechin Gallate Exposures In Cirrhotic And Noncirrhotic Patients With Hepatitis C After Single Oral Doses Of Green Tea Extract., Dina Halegoua-De Marzio, Walter K. Kraft, Constantine Daskalakis, Xie Ying, Roy L Hawke, Victor J. Navarro

Division of Gastroenterology and Hepatology Faculty Papers

BACKGROUND: Epigallocatechin-3-gallate (EGCG) has antiangiogenic, antioxidant, and antifibrotic properties that may have therapeutic potential for the treatment of cirrhosis induced by hepatitis C virus (HCV). However, cirrhosis might affect EGCG disposition and augment its reported dose-dependent hepatotoxic potential.

OBJECTIVE: The safety, tolerability, and disposition of a single oral dose of EGCG in cirrhotic patients with HCV were examined in an exploratory fashion.

METHODS: Eleven patients with hepatitis C and detectable viremia were enrolled. Four had Child-Pugh (CP) class A cirrhosis, 4 had Child-Pugh class B cirrhosis, and 3 were noncirrhotic. After a single oral dose of green tea extract 400 …

Nonparametric Inference For Meta Analysis With Fixed Unknown, Study-Specific Parameters, Brian Claggett, Minge Xie, Lu Tian

Harvard University Biostatistics Working Paper Series

No abstract provided.

Statistical Inference When Using Data Adaptive Estimators Of Nuisance Parameters, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In order to be concrete we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing n independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically …

Treatment Selections Using Risk-Benefit Profiles Based On Data From Comparative Randomized Clinical Trials With Multiple Endpoints, Brian Claggett, Lu Tian, Davide Castagno, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Likelihood Ratio Tests For The Mean Structure Of Correlated Functional Processes, Ana-Maria Staicu, Yingxing Li, Ciprian Crainiceanu, David M. Ruppert

Johns Hopkins University, Dept. of Biostatistics Working Papers

The paper introduces a general framework for testing hypotheses about the structure of the mean function of complex functional processes. Important particular cases of the proposed framework are: 1) testing the null hypotheses that the mean of a functional process is parametric against a nonparametric alternative; and 2) testing the null hypothesis that the means of two possibly correlated functional processes are equal or differ by only a simple parametric function. A global pseudo likelihood ratio test is proposed and its asymptotic distribution is derived. The size and power properties of the test are confirmed in realistic simulation scenarios. Finite …

Longitudinal Functional Models With Structured Penalties, Madan G. Kundu, Jaroslaw Harezlak, Timothy W. Randolph

Johns Hopkins University, Dept. of Biostatistics Working Papers

Collection of functional data is becoming increasingly common including longitudinal observations in many studies. For example, we use magnetic resonance (MR) spectra collected over a period of time from late stage HIV patients. MR spectroscopy (MRS) produces a spectrum which is a mixture of metabolite spectra, instrument noise and baseline profile. Analysis of such data typically proceeds in two separate steps: feature extraction and regression modeling. In contrast, a recently-proposed approach, called partially empirical eigenvectors for regression (PEER) (Randolph, Harezlak and Feng, 2012), for functional linear models incorporates a priori knowledge via a scientifically-informed penalty operator in the regression function …

Group Testing Regression Models, Boan Zhang

Department of Statistics: Dissertations, Theses, and Student Research

Group testing, where groups of individual specimens are composited to test for the presence or absence of a disease (or some other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Statistical research in group testing has traditionally focused on a homogeneous population, where individuals are assumed to have the same probability of having a disease. However, individuals often have different risks of positivity, so recent research has examined regression models that allow for heterogeneity among individuals within the population. This dissertation focuses on two problems involving group testing regression models. …

Pls-Rog: Partial Least Squares With Rank Order Of Groups, Hiroyuki Yamamoto

COBRA Preprint Series

Partial least squares (PLS), which is an unsupervised dimensionality reduction method, has been widely used in metabolomics. PLS can separate score depend on groups in a low dimensional subspace. However, this cannot use the information about rank order of groups. This information is often provided in which concentration of administered drugs to animals is gradually varies. In this study, we proposed partial least squares for rank order of groups (PLS-ROG). PLS-ROG can consider both separation and rank order of groups.

Statistical Hypothesis Test Of Factor Loading In Principal Component Analysis And Its Application To Metabolite Set Enrichment Analysis, Hiroyuki Yamamoto, Tamaki Fujimori, Hajime Sato, Gen Ishikawa, Kenjiro Kami, Yoshiaki Ohashi

COBRA Preprint Series

Principal component analysis (PCA) has been widely used to visualize high-dimensional metabolomic data in a two- or three-dimensional subspace. In metabolomics, some metabolites (e.g. top 10 metabolites) have been subjectively selected when using factor loading in PCA, and biological inferences for these metabolites are made. However, this approach is possible to lead biased biological inferences because these metabolites are not objectively selected by statistical criterion. We proposed a statistical procedure to pick up metabolites by statistical hypothesis test of factor loading in PCA and make biological inferences by metabolite set enrichment analysis (MSEA) for these significant metabolites. This procedure depends …

Decline In Health For Older Adults: 5-Year Change In 13 Key Measures Of Standardized Health, Paula H. Diehr, Stephen M. Thielke, Anne B. Newman, Calvin H. Hirsch, Russell Tracy

UW Biostatistics Working Paper Series

Introduction

The health of older adults declines over time, but there are many ways of measuring health. We examined whether all measures declined at the same rate, or whether some aspects of health were less sensitive to aging than others.

Methods

We compared the decline in 13 measures of physical, mental, and functional health from the Cardiovascular Health Study: hospitalization, bed days, cognition, extremity strength, feelings about life as a whole, satisfaction with the purpose of life, self-rated health, depression, digit symbol substitution test, grip strength, ADLs, IADLs, and gait speed. Each measure was standardized against self-rated health. We compared …

Methods For Evaluating Prediction Performance Of Biomarkers And Tests, Margaret Pepe, Holly Janes

UW Biostatistics Working Paper Series

This chapter describes and critiques methods for evaluating the performance of markers to predict risk of a current or future clinical outcome. We consider three criteria that are important for evaluating a risk model: calibration, benefit for decision making and accurate classification. We also describe and discuss a variety of summary measures in common use for quantifying predictive information such as the area under the ROC curve and R-squared. The roles and problems with recently proposed risk reclassification approaches are discussed in detail.

The Impact Of Covariance Misspecification In Multivariate Gaussian Mixtures On Estimation And Inference: An Application To Longitudinal Modeling, Brianna C. Heggeseth, Nicholas P. Jewell

U.C. Berkeley Division of Biostatistics Working Paper Series

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence---that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little …

Borrowing Information Across Populations In Estimating Positive And Negative Predictive Values, Ying Huang, Youyi Fong, John Wei, Ziding Feng

UW Biostatistics Working Paper Series

A marker's capacity to predict risk of a disease depends on disease prevalence in the target population and its classification accuracy, i.e. its ability to discriminate diseased subjects from non-diseased subjects. The latter is often considered an intrinsic property of the marker; it is independent of disease prevalence and hence more likely to be similar across populations than risk prediction measures. In this paper, we are interested in evaluating the population-specific performance of a risk prediction marker in terms of positive predictive value (PPV) and negative predictive value (NPV) at given thresholds, when samples are available from the target population …

Causal Inference For Networks, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Suppose that we observe a population of causally connected units according to a network. On each unit we observe a set of potentially connected units that contains the true connections, and a longitudinal data structure, which includes time-dependent exposure or treatment, time-dependent covariates, a final outcome of interest. The target quantity of interest is defined as the mean outcome for this group of units if the exposures of the units would be probabilistically assigned according to a known specified mechanism, where the latter is called a stochastic intervention. Causal effects of interest are defined as contrasts of the mean of …

Targeted Learning Of The Probability Of Success Of An In Vitro Fertilization Program Controlling For Time-Dependent Confounders, Antoine Chambaz, Sherri Rose, Jean Bouyer, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Infertility is a global public health issue and various treatments are available. In vitro fertilization (IVF) is an increasingly common treatment method, but accurately assessing the success of IVF programs has proven challenging since they consist of multiple cycles. We present a double robust semiparametric method that incorporates machine learning to estimate the probability of success (i.e., delivery resulting from embryo transfer) of a program of at most four IVF cycles in the French Devenir Apr`es Interruption de la FIV (DAIFI) study and several simulation studies, controlling for time-dependent confounders. We find that the probability of success in the DAIFI …

Assessing The Causal Effect Of Policies: An Approach Based On Stochastic Interventions, Iván Díaz, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Stochastic interventions are a powerful tool to define parameters that measure the causal effect of a realistic intervention that intends to alter the population distribution of an exposure. In this paper we follow the approach described in D\'iaz and van der Laan (2011) to define and estimate the effect of an intervention that is expected to cause a truncation in the population distribution of the exposure. The observed data parameter that identifies the causal parameter of interest is established, as well as its efficient influence function under the non parametric model. Inverse probability of treatment weighted (IPTW), augmented IPTW and …

A Study Of Cellular Calcium Dynamics In Culture Using Fluorescence Microscopy – A Statistical And Mathematical Approach, Richard Adekola Idowu

Doctoral Dissertations

Calcium in its ionic form is very dynamic, especially in excitable cells such as muscle and brain cells, moving from the high concentration exterior of the cell to much lower concentrations inside the cell, where calcium is used as a second messenger. In brain cells, and neurons especially, calcium is a key signaling ion involved in memory and learning with excitatory neurotransmitters such as glutamate turning neurons "on." Glutamate excites the neurons in part by causing large and dynamic changes in the intracellular calcium concentration. While these dynamics are essential for normal signaling in the brain, excessive and sustained elevations …

Quantifying Alternative Splicing From Paired-End Rna-Sequencing Data, David Rossell, Camille Stephan-Otto Attolini, Manuel Kroiss, Almond Stöcker

COBRA Preprint Series

RNA-sequencing has revolutionized biomedical research and, in particular, our ability to study gene alternative splicing. The problem has important implications for human health, as alternative splicing is involved in malfunctions at the cellular level and multiple diseases. However, the high-dimensional nature of the data and the existence of experimental biases pose serious data analysis challenges. We find that the standard data summaries used to study alternative splicing are severely limited, as they ignore a substantial amount of valuable information. Current data analysis methods are based on such summaries and are hence sub-optimal. Further, they have limited flexibility in accounting for …

Effects Of Genetic Variants Previously Associated With Fasting Glucose And Insulin In The Diabetes Prevention Program, Jose C. Florez, Kathleen A. Jablonski, Jarred B. Mcateer, Paul W. Franks, Clinton C. Mason, Kieren J. Mather, Edward Horton, Ronald Goldberg, Dana Dabelea, Steven E. Kahn, Richard F. Arakaki, Alan R. Shuldiner, William C. Knowler

Epidemiology Faculty Publications

Common genetic variants have been recently associated with fasting glucose and insulin levels in white populations. Whether these associations replicate in pre-diabetes is not known. We extended these findings to the Diabetes Prevention Program, a clinical trial in which participants at high risk for diabetes were randomized to placebo, lifestyle modification or metformin for diabetes prevention. We genotyped previously reported polymorphisms (or their proxies) in/near G6PC2, MTNR1B, GCK, DGKB, GCKR, ADCY5, MADD, CRY2, ADRA2A,FADS1, PROX1, SLC2A2, GLIS3, C2CD4B, IGF1, and IRS1 in 3,548 Diabetes …