Graduate Biostatistics Courses

BMS512 Principles of Epidemiology (3 Credits)

Topics covered in this course include: basic principles of epidemiology; measures of disease frequency; epidemiologic study designs: experimental and observational; bias; confounding; outbreak investigations; screening; causality; and ethical issues in epidemiologic research. In addition, students will develop skills to read, interpret and evaluate health information from published epidemiologic studies. We will also discuss the biological, behavioral, sociocultural and environmental factors associated with the etiology and distribution of health and disease.

BMS542 Public Health Foundations (3 Credits)

This course will introduce students to the history and role of public health, and will set the foundation of public health practice on the two guiding paradigms: the 10 essential public health functions, and the One Health approach to public health prevention and problem solving. Students build their public health competency via investigating a breadth of public health issues, including both chronic and infectious disease, and the impact of our environment and various factors on disease spread, acquisition, and impact. The course will also introduce the organization and delivery of the American healthcare system.

BST501 Statistical Methods In Epidemiology (3 Credits)

Statistical Methods in Epidemiology is a comprehensive course in concepts in epidemiology and statistics, epidemiologic study designs, statistical techniques, and epidemiologic applications.

BST511 Categorical Data Analysis (3 Credits)

This course surveys theory and methods for the analysis of categorical response and count data. The course begins with an overview of likelihood-based inference for categorical data analysis. Methods for describing and analyzing contingency tables are surveyed. These include log-linear modeling of association structures, the Cochran-Mantel-Haenszel approach to detecting conditional association, and multinomial-Poisson homogeneous modeling. Dichotomous response models such as the logistic regression model will be described and applied in several settings including cohort and case-control studies. Poisson regression models will be used to analyze rate data from event history studies. Ordinal and polytomous response models such as the cumulative and multinomial logit models will also be introduced. Time permitting, these regression models will be adapted and extended to accommodate longitudinal data.

BST521 Advanced Regression Analysis for Public Health Studies (3 Credits)

Advanced Regression Analysis for Public Health Studies is a comprehensive course in concepts, study designs, and regression analysis techniques in public health studies.

BST621 Sampling Methods (3 Credits)

This course will cover a wide range of statistical sampling techniques that are used to make inferences about a population. We will start with the most basic of designs: a simple random sample, then quickly add complexity with stratification, clustering, and unequal selection probabilities. We then discuss how to form estimates of unknown population parameters and quantify our sampling error when data is collected using a complex sampling design. By the end of the term you will know how to graph, run chi-square tests, and fit basic regressions models for complex survey data. The R “survey” package will be used extensively throughout the course.

BST631 Real World Health Care Data Analysis (3 Credits)

Real World Health Care Data Analysis is a comprehensive course in principles and methodologies in design, conduct, analysis and evaluation of real world health care observational studies. The real world health care data include many different types and sources, usually big data, such as electronic health records (EHR), health insurance claims and billing data, drug and disease registries data, and data gathered through personal devices and health applications.

BST671 GIS And Spatial Analysis For Public Health (3 Credits)

This course is an introduction to Geographic Information Systems (GIS) and its application for public health research. Classwork will be presented in the form of health-related case studies based on research topics pertinent to students in the School of Public Health, where GIS is used to formulate and address scientific hypotheses. Specifically, the ArcGIS software will be presented as a tool for integrating, manipulating, and displaying spatial health data. Topics include understanding spatial data, mapping, topology, spatial manipulations related to data structures, online data, geocoding, remote sensing imagery, and mobile technology. The course will emphasize how to prepare spatial data for a formal statistical analysis, which will be discussed at an introductory level for geostatistical, point pattern, and area-level (or lattice) data examples.

BST731 Clinical Trial: Design And Analysis Of Medical Experiments (3 Credits)

Clinical Trial: Design and Analysis of Medical Experiments is a comprehensive course in principles and methodologies in design, conduct, analysis and evaluation of clinical trials.

BST741 Statistical Methods In Genetics (3 Credits)

Statistical Methods in Genetics is a comprehensive course in the applied statistical methods for discrete genetic data, population genetics data, and quantitative genetics data.

BST751 BST Thesis (6 Credits)

The BST Thesis provides an opportunity for students to apply the knowledge and skills gained throughout the program to a real-world problem or project. Through a collaborative experience involving applied biostatistics in the form of real-world data analysis and methods, students will delve into topics that will help align with future careers.

STA539 Time Series (3 Credits)

This course introduces the basic time series analysis and forecasting methods. Topics include time series regression and exploratory data analysis, stationary processes, ARMA/ARIMA models, spectral analysis, model and forecasting using ARMA/ARIMA models, nonstationary and seasonal time series models, multivariate time series, state-space models, and forecasting techniques.

STA571 Advanced Statistical Computing (3 Credits)

The course will cover advanced computational algorithms designed primarily for fitting complex Bayesian hierarchical models. These include MCMC, variational inference, Hamiltonian Monte Carlo, stochastic optimization among others. Lectures involve a general description of methodology followed by demonstration of algorithms. The computational techniques will be discussed without a concrete focus on a particular programming language. General guidance will be provided on how to make the codes/algorithms efficient, devise and run large scale simulations and submit jobs to high performance computing.

STA637 Generalized Linear Models (3 Credits)

This course will introduce the statistical theory and methods to extend regression to non-normal data. This course covers the construction and estimation of parameters in generalized linear models, including specific treatment of nominal and ordinal logistic regression, log linear models, Poisson regression, gamma regression, models for dependent data, and other topics as time permits.

STA641 Applied Longitudinal Data Analysis (3 Credits)

This course is designed to provide an overview of statistical models and methodologies for analyzing repeated measures with a particular emphasis on analyzing longitudinal data. Its analysis requires much more sophisticated methodologies due to the correlation among observations. This course covers modern statistical techniques for longitudinal data from an applied perspective. Topics include characteristics of the longitudinal design, graphical exploration of the mean and correlation structure, linear mixed effects models and multilevel modeling, maximum likelihood and restricted maximum likelihood estimation, modeling the variance-covariance structures, inference for random effects, logistic and Poisson mixed effects model for binary and count data, marginal models and generalized estimating equations, and model diagnostics. Analysis of real and substantial data sets using statistical software SAS will be integrated throughout.

MS in Biostatistics  ›  Graduate Biostatistics Courses