Statistics and Probability Commons^™

Ultrahigh Dimensional Time Course Feature Selection, Peirong Xu, Lixing Zhu, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

Statistical challenges arise from modern biomedical studies that produce time course genomic data with ultrahigh dimensions. In a renal cancer study that motivated this paper, the pharmacokinetic measures of a tumor suppressor (CCI-779) and expression levels of 12625 genes were measured for each of 33 patients at 8 and 16 weeks after the start of treatments, with the goal of identifying predictive gene transcripts and the interactions with time in peripheral blood mononuclear cells for pharmacokinetics over the time course. The resulting dataset defies analysis even with regularized regression. Although some remedies have been proposed for both linear and generalized …

Selection Of Latent Variables For Multiple Mixed-Outcome Models, Ling Zhou, Huazhen Lin, Xin-Yuan Song, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. As the formulation of a latent variable model is often unknown a priori, misspecification could distort the dependence structure and lead to unreliable model inference. More- over, the multiple outcomes are often of varying types (e.g., continuous and ordinal), which presents analytical challenges. In this article, we present a class of general latent variable models that can accommodate mixed types of outcomes, and further propose a novel selection approach that simultaneously selects latent variables and estimates model parameters. We show that the proposed estimators …

Semiparametric Latent Variable Transformation Models For Multiple Mixed Outcomes, Huazhen Lin, Ling Zhou, Robert Elashoff, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

No abstract provided.

Semiparametric Transformation Models For Semicompeting Survival Data, Huazhen Lin, Ling Zhou, Chunhong Li, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

Semicompeting risk outcome data, e.g. time to disease progression and time to death, are commonly collected in clinical trials, but complicated analytical tools hamper the analysis and the interpretation of the results. We propose a novel semiparametric transformation model for such data. Compared with the existing models, our model is advantageous in the following distinctive ways. First, it allows us to provide direct estimators of the regression analysis and the association parameter. Second, the measure of surrogacy, for example, the proportion of treatment effect and relative effect, can also be directly obtained. We propose a two-stage estimation procedure for inference …

Score Test Variable Screening, Sihai Dave Zhao, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

Variable screening has emerged as a crucial first step in the analysis of high-throughput data, but existing procedures can be computationally cumbersome, difficult to justify theoretically, or inapplicable to certain types of analyses. Motivated by a high-dimensional censored quantile regression problem in multiple myeloma genomics, this paper makes three contributions. First, we establish a score test-based screening framework, which is widely applicable, extremely computationally efficient, and relatively simple to justify. Secondly, we propose a resampling-based procedure for selecting the number of variables to retain after screening according to the principle of reproducibility. Finally, we propose a new iterative score test …

A Latent Variable Transformation Model Approach For Exploring Dysphagia, Anna Snavely, David P. Harrington, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

No abstract provided.

Covariance-Enhanced Discriminant Analysis, Peirong Xu, Ji Zhu, Lixing Zhu, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

Linear discriminant analysis (LDA), a classical method in pattern recognition and machine learning, has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high-dimensionality of the high-throughput features obtained from modern biological experiments, for example, microarray or proteomics, defies traditional discriminant analysis techniques. The possible interfeature correlations present additional challenges and are often under-utilized in modeling. In this paper, by incorporating the possible inter-feature correlations, we propose a Covariance-Enhanced Discriminant Analysis (CEDA) method that simultaneously and consistently selects informative features and identifies the corresponding discriminable classes. We show that, under mild regularity conditions, …

A Method For Generating Realistic Correlation Matrices, Johanna S. Hardin, Stephan Ramon Garcia, David Golan

Pomona Faculty Publications and Research

Simulating sample correlation matrices is important in many areas of statistics. Approaches such as generating Gaussian data and finding their sample correlation matrix or generating random uniform $[-1,1]$ deviates as pairwise correlations both have drawbacks. We develop an algorithm for adding noise, in a highly controlled manner, to general correlation matrices. In many instances, our method yields results which are superior to those obtained by simply simulating Gaussian data. Moreover, we demonstrate how our general algorithm can be tailored to a number of different correlation models. Using our results with a few different applications, we show that simulating correlation matrices …

Generalized Least-Squares Regressions I: Efficient Derivations, Nataniel Greene

Publications and Research

Ordinary least-squares regression suffers from a fundamental lack of symmetry: the regression line of y given x and the regression line of x given y are not inverses of each other. Alternative symmetric regression methods have been developed to address this concern, notably: orthogonal regression and geometric mean regression. This paper presents in detail a variety of least squares regression methods which may not have been known or fully explicated. The derivation of each method is made efficient through the use of Ehrenberg's formula for the ordinary least-squares error and through the extraction of a weight function g(b) which characterizes …

Information And Communication Technology To Link Criminal Justice Reentrants To Hiv Care In The Community, Ann Kurth, Irene Kuo, James Peterson, Nkiru Azikiwe, Lauri Bazerman, Alice Cates, Curt G. Beckwith

Epidemiology Faculty Publications

The United States has the world’s highest prison population, and an estimated one in seven HIV-positive persons in the USA passes through a correctional facility annually. Given this, it is critical to develop innovative and effective approaches to support HIV treatment and retention in care among HIV-positive individuals involved in the criminal justice (CJ) system. Information and communication technologies (ICTs), including mobile health (mHealth) interventions, may offer one component of a successful strategy for linkage/retention in care. We describe CARE+ Corrections, a randomized controlled trial (RCT) study now underway in Washington, that will evaluate the combined effect of computerized motivational …

Complex Dynamics In Predator-Prey Models With Nonmonotonic Functional Response And Seasonal Harvesting, Jicai Huang, Jing Chen, Yijun Gong, Weipeng Zhang

Mathematics Faculty Articles

In this paper we study the complex dynamics of predator-prey systems with nonmonotonic functional response and harvesting. When the harvesting is constant-yield for prey, it is shown that various kinds of bifurcations, such as saddle-node bifurcation, degenerate Hopf bifurcation, and Bogdanov-Takens bifurcation, occur in the model as parameters vary. The existence of two limit cycles and a homoclinic loop is established by numerical simulations. When the harvesting is seasonal for both species, sufficient conditions for the existence of an asymptotically stable periodic solution and bifurcation of a stable periodic orbit into a stable invariant torus of the model are given. …

Sources Of The Persistent Gender Wage Gap Along The Unconditional Earnings Distribution: Findings From Kenya, Richard U. Agesa, Jacqueline Agesa, Andrew Dabalen

Economics Faculty Research

Past studies on gender wage inequality in Africa typically attribute the gender pay gap either to gender differences in characteristics or in the return to characteristics. The authors suggest, however, that this understanding of the two sources may be far too general and possibly overlook the underlying covariates that drive the gender wage gap. Moreover, past studies focus on the gender wage gap exclusively at the conditional mean. The authors go further to evaluate the partial contribution of each wage-determining covariate to the magnitude of the gender pay gap along the unconditional earnings distribution. The authors' data are from Kenya, …

Determinants Of Negative Pathways To Care And Their Impact On Service Disengagement In First-Episode Psychosis., Kelly K. Anderson, Rebecca Fuhrer, Norbert Schmitz, Ashok K Malla

Epidemiology and Biostatistics Publications

PURPOSE: Although there have been numerous studies on pathways to care in first-episode psychosis (FEP), few have examined the determinants of the pathway to care and its impact on subsequent engagement with mental health services.

METHODS: Using a sample of 324 FEP patients from a catchment area-based early intervention (EI) program in Montréal, we estimated the association of several socio-demographic, clinical, and service-level factors with negative pathways to care and treatment delay. We also assessed the impact of the pathway to care on time to disengagement from EI services.

RESULTS: Few socio-demographic or clinical factors were predictive of negative pathways …

Comparison Of Molecular Breeding Values Based On Within- And Across-Breed Training In Beef Cattle, Stephen D. Kachman, Matthew L. Spangler, Gary L. Bennett, Kathryn J. Hanford, Larry A. Kuehn, Warren M. Snelling, Mark Thallman, Mahdi Saatchi, Dorian J. Garrick, Robert D. Schnabel, Jeremy F. Taylor, E John Pollak

Department of Statistics: Faculty Publications

Background: Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported.

Methods: Molecular breeding values for weaning and yearling weight …

Obesity And Preference-Weighted Quality Of Life Of Ethnically Diverse Middle School Children: The Healthy Study, Roberto P. Trevino, Trang H. Pham, Sharon Edelstein

GW Biostatistics Center

No abstract provided.

Transmission Potential Of Influenza A/H7n9, February To May 2013, China, Gerardo Chowell, Lone Simonsen, Sherry Towers, Mark A. Miller, Cecile G. Viboud

Epidemiology Faculty Publications

Background

On 31 March 2013, the first human infections with the novel influenza A/H7N9 virus were reported in Eastern China. The outbreak expanded rapidly in geographic scope and size, with a total of 132 laboratory-confirmed cases reported by 3 June 2013, in 10 Chinese provinces and Taiwan. The incidence of A/H7N9 cases has stalled in recent weeks, presumably as a consequence of live bird market closures in the most heavily affected areas. Here we compare the transmission potential of influenza A/H7N9 with that of other emerging pathogens and evaluate the impact of intervention measures in an effort to guide pandemic …

A Case–Control Study Of Incident Rheumatological Conditions Following Acute Gastroenteritis During Military Deployment, Kathryn Deyoung, Mark A. Riddle, Larissa S. May, Chad K. Porter

Epidemiology Faculty Publications

Objectives The aim of this study was to assess the risk of incident rheumatological diagnoses (RD) associated with self-reported diarrhoea and vomiting during a first-time deployment to Iraq or Afghanistan. Such an association would provide evidence that RD in this population may include individuals with reactive arthritis (ReA) from deployment-related infectious gastroenteritis.

Design This case–control epidemiological study used univariate and multivariate logistic regression to compare the odds of self-reported diarrhoea/vomiting among deployed US military personnel with incident RD to the odds of diarrhoea/vomiting among a control population.

Setting We analysed health records of personnel deployed to Iraq or Afghanistan, including …

Statistical Analysis Of Microarray Data In Sleep Deprivation, Stephanie Marie Berhorst

Masters Theses

"Microarray technology is a useful tool for studying the expression levels of thousands of genes or exons within a single experiment. Data from microarray experiments present many challenges for researchers since costly resources often limit the experimenter to small sample sizes and large amounts of data are generated. The researcher must carefully consider the appropriate statistical analysis to use that aligns with the experimental design employed. In this work, statistical issues are investigated and addressed for a microarray experiment that examines how expression levels change over time as individuals are sleep deprived. Over the course of 48 hours of sleep …

Estimation And Q-Matrix Validation For Diagnostic Classification Models, Yuling Feng

Theses and Dissertations

Diagnostic classification models (DCMs) are structured latent class models widely discussed in the field of psychometrics. They model subjects' underlying attribute patterns and classify subjects into unobservable groups based on their mastery of attributes required to answer the items correctly. The effective implementation of DCMs depends on correct specification of a Q-matrix which is a binary matrix linking attribute patterns to items. Current literature on assessing the appropriateness of Q-matrix specifications has focused on validation methods for the deterministic-input, noisy-and-gate (DINA) model. The goal of the study is to develop general Q-matrix validation methods that can be applied to a …

Heaped Data In Count Models, Tammy Harris

Theses and Dissertations

Heaped data result when subjects who recall the frequency of events prefer for reporting from a limited set of rounded responses or preferred digits over reporting exact counts. These rounded responses and digit preferences (also referred to as data coarsening) could be characterized by reported frequencies (or counts) favoring multiples of 20, reporting counts ending with 0 or 5, or a preference for reporting an even number over an odd number or vice versa. This mixture of values is a type of measurement error (pattern of misreporting) that can lead to biased estimation and imprecision in discrete quantitative data. Sometimes …