Courses
All courses carry 3 credits unless otherwise specified.
BIOSTATS 540 Introductory Biostatistics
Introduction to basic statistical literacy. We begin with a discussion of the ideas of variability in nature and the tools we used for its description. The distinction between systematic versus chance variability is introduced. Topics include: graphical and numerical description, random sampling and selected probability distributions (uniform, bernoulli, binomial and normal), confidence interval estimation, the basics of statistical hypothesis testing (Z, t, F, and chi square for one and two samples), simple linear regression and correlation. Pre-requisites: none.
BIOSTATS 590A ST-Advanced Statistical Computing in R
Prepare students with advanced computing skills for a career as a statistician or data analyst/scientist. By the end of this course, you should be able to have mastery over the fundamentals of the R programming language, including concepts such as functional programming and meta programming. Credit, 1.
BIOSTATS 597D Introduction to Statistical Computing in R
Introductory course. Foundational training in the modern tools of statistical computing and reproducible research using R. Manipulate, summarize, analyze, and visualize data using R. Credit, 1.
BIOSTATS 597E Intermediate Statistical Computing
Prepare students with necessary computing skills for a career as a statistician or data analyst/scientist. Students will learn to use various tools to extract data from different sources (structured or unstructured), and transform them into forms that are ready for analysis and modeling. Students will also be able to build web based tools to deliver data products using R Shiny. Credit, 1.
EPI 630 Principles of Epidemiology
Introductory course. An epidemiological perspective on health. General approaches for describing patterns of disease in groups of people, and elucidating various processes involved in creating differing levels of health in human groups. Lecture and lab examples of a wide range of contemporary health problems. Prerequisite: none.
EPI 631 Scientific Writing for Thesis, Dissertation and Grant Proposals in Epidemiology
Provides students with the necessary analytic techniques, technical resources, and writing expertise to design and write thesis proposal and final thesis manuscripts in the field of epidemiology. Students prepare a written proposal and a class presentation, and critique another presentations. Prerequisite: EPI 630.
EPI 632 Applied Epidemiology
Intermediate level course. Application of epidemiologic methods to study the etiology, control, and impact on society of selected diseases. Prerequisite: EPI 630.
EPI 633 Communicable Disease Epidemiology
Review of selected infectious diseases; emphasis on current theories of distribution, transmission, and control. Prerequisite: EPI 630.
EPI 634 Nutritional Epidemiology
Epidemiologic study design problems and issues; major methods of dietary assessment; non-dietary nutritional assessments; and the relative strength of evidence in support of diet-disease relationships. Prerequisite: PUBHLTH 630.
EPI 635 Social Epidemiology
Links between life styles and risks to which individuals in populations are vulnerable. Models linking social stress and physiological responses, psychosocial mediators, and social support systems as they promote or reduce susceptibility to disease. Prerequisite: EPI 630.
EPI 636 Epidemiological Assessment
Methods for epidemiological assessment of the efficacy and safety of medical technologies, including drugs, devices, and medical and surgical procedures. Prerequisite: EPI 630.
EPI 639 Cancer Epidemiology
Background in the principles of oncology and a review of epidemiological strategies used in cancer research. The major cancer risk factors and the key strategies of prevention. Prerequisite: EPI 630.
BIOSTATS 640 Intermediate Biostatistics
The objective is the further development of basic statistical literacy and basic skills in the analysis of biological and health data. Use of statistical software (R and Stata) and the analysis of data sets are included. Topics include: simple linear regression, multivariate regression, analysis of proportions and rates, logistic regression, survival analysis, analysis of variance, and nonparametrics. Time permitting, mixed models analyses are also introduced. Pre-requisites: BIOSTATS 540 or equivalent, or permission of the instructor.
EPI 640 Reproductive Epidemiology
Introduction to areas of active research in the field of reproductive health focusing on their public health significance, descriptive epidemiology etiology and prevention. Addresses both methodologic and substantive challenges to reproductive and perinatal research. Prerequisite: EPI 630.
BIOSTATS 650 Biostatistics Methods 2: Applied Regression Modeling
Intermediate course. Theory and application of linear regression and generalized linear regression models. Examples and exercises from scientific, medical, and public health research. Prerequisite: BIOSTATS 540.
BIOSTATS 690B Introduction to Causal Inference
Introduce a general framework for causal inference: 1) clear statement of the scientific question, 2) definition of the causal model and parameter of interest, 3) assessment of identifiability, 4) choice and implementation of estimators including parametric and semi-parametric methods, and 5) interpretation of findings. The methods include G-computation, inverse probability of treatment weighting (IPTW), and targeted maximum likelihood estimation (TMLE) with Super Learning. Students gain practical experience implementing these estimators and interpreting results through discussion assignments, R labs, and R assignments. Requirements: A course in intermediate biostatistics and experience with regression modeling; or instructor permission. Recommended: a course in intermediate epidemiology.
EPI 690E Environmental Epidemiology
Explore contemporary topics and methods in studying the link between the physical environment and population’s health. Many chronic diseases are of unknown or multifactorial etiology but may likely be related to environmental exposures. It focuses on the ways environmental factors affect the health of populations. Topical areas include effects of air pollution, pesticides, metals, and endocrine disrupting chemicals on a wide range of health outcomes, including, neurodevelopment, reproductive, and metabolic functions. It will include recent advancements in studying these topics and will include: critical appraisal of exposure assessment methods, statistical, and epidemiological methods used in environmental epidemiology studies will be considered.
EPI 690EW Epidemiology of Women's Health
Overview of current issues in women's health throughout the life cycle. Exploring how epidemiologic methods are used to evaluate factors influencing reproductive health, cancer, cardiovascular disease and other common disorders. Students learn basic quantitative methods, study design concepts, and critical thinking skills. Prerequisite: EPI 630.
BIOSTATS 690JQ Biostatistics Methods 3: Modern Applied Statistical Learning
Advanced Course. Statistical modeling approaches including: penalized regression, methods for classification, statistical methods for biomarker discovery, robust regression, and flexible regression methods. Prerequisite: BIOSTATS 540
BIOSTATS 690MS Applied Stochastic Models in Population Geonomics
Introduction to stochastic models used in Population Genomics to study the evolutionary forces that shape genetic variation.
EPI 690R Introduction to Epidemiologic Research Methods
This course provides an overview of fundamental research methods utilized in conducting epidemiologic studies. Prerequisite: none.
BIOSTATS 690T Applied Statistical Genetics
This course will provide fundamental statistical concepts and tools relevant to the analysis of high-dimensional genomics data arising from population-based association studies. A first-course in statistics is assumed.
BIOSTATS 690Z Health Data Science: Statistical Modeling
Students who want to learn essential statistical and computational skills for health data science. Students will obtain hands-on experience in implementing a wide range of commonly used statistical methods with real data from public health and biomedical research using the statistical programming language R. The course motivates statistical reasoning and methods through real health data. The focus of the course is to train students in refining a scientific question into a statistical framework, choosing proper regression models, writing scripts and executing them in R, and interpreting scientifically meaningful findings.
BIOSTATS 691F Data Management and Analysis Using SAS
In order to do anything useful in biostatistics or epidemiology, we have analyze it and read the results that come from statistical software packages. Competing packages have complementary strengths and weaknesses. SAS software has clear documentation and accurate computations. In this course you will become comfortable with using SAS for basic data manipulation and analysis. You will also reinforce your understanding of some key statistical concepts. By the end of this course, students will use SAS to: 1) read data from many formats; 2) generate univariate statistics and histograms; 3) define new variables using logic and functions; 4) merge and subset data sets; 5) make bivariate tables and scatterplots; 6) test for association; 8) perform linear and logistic regression.
EPI 691P Seminar - Physical Activity
Epidemiologic methods in studies of physical activity. Seminar will cover measurement of physical activity and inactivity; establishing validity and reliability of physical activity; design of present-day epidemiologic studies of physical activity and health; and physical activity surveillance.
696D Independent Study in Public Health
Special investigational or research problems for M.P.H. candidates or advanced students. Scope of the work can be varied to meet specified conditions. Credit, 1-6.
698 Practicum
Opportunity for supervised field observation to gain practice experience in selected public health agencies.
699 Master’s Thesis
Independent research leading to a thesis on a public health subject. Results should be suitable for publication. Credit, 1-9.
EPI 700 Analysis of Epidemiologic Data
Students will develop fundamental skills in data analysis and interpretation. A major emphasis will be to gain practical experience in analyzing data using statistical software. Prerequisite: EPI 630, BIOSTATS 540, and BIOSTATS 691F
BIOSTATS 730 Applied Bayesian Statistical Modeling
Theory and application of Bayesian methods for analysis of biomedical datasets. Bayesian thinking, estimation of single and multi-parameter models and Bayesian computation using Markov Chain Monte Carlo (MCMC) methods.
EPI 737 Intermediate Methods in Epidemiology
A methodologic core course. Details of concepts and quantitative techniques used in modern epidemiology. Prerequisites: EPI 630 and 632.
BIOSTATS 740 Mixed Models and Longitudinal Data Analysis
In this course we explore statistical approaches to the setting in which the assumption of independent observations is violated. In this course you will learn what the implications of correlated data are, from a statistical perspective, how to think about them, how to analyze them and interpret the data analysis, and the theoretical underpinnings of the data analysis approaches. You will primarily use mixed models in SAS and R. By the end of this course, you will 1) recognize correlated data occurring by design or chance; 2) diagnose the likely effects of ignoring correlation in data analysis; 3) learn about mean models for longitudinal data; 4) learn about mixed models; 5) understand the theoretical bases of the methods employed. Pre-requisites: 650 or similar; SAS; R
BIOSTATS 743 Analysis of Categorical Data in the Health Sciences
An overview of statistical methods for analyzing data where the outcome variable is categorical or discrete. The course will emphasize the theoretical underpinnings of the methods as well as an applied understanding of the computation and interpretation, both of which are necessary to succeed with real data analysis. We will cover inference for binomial and multinomial variable with contingency tables, generalized linear models, logistic regression for binary responses, logit models for multiple response categories, log-linear models, some statistical machine learning approaches, inference for matched-pairs, and correlated/clustered data. Examples will be taken from public health and biomedical research. Prerequisite: BIOSTATS 540, STAT, 515, STAT 516, BIOSTATS 650/525, or equivalent coursework.
BIOSTATS 744 Computer Analysis of Health Sciences Data
Applications of the linear regression model. Emphasis on use and interpretation of statistical software output. Prerequisite: BIOSTATS 640.
BIOSTATS 745 Sampling Methods for the Health Sciences
Application of widely used sampling methods to situations commonly occurring in public health research. Alternative sampling strategies compared; emphasis on design of sample surveys. Types of samples stressed: simple random sample, stratified sample, systematic sample, and cluster sample. Also the combined ratio estimate, and large-scale, ongoing sample surveys such as the Health Examination Survey of the National Center for Health Statistics. Prerequisite: BIOSTATS 540.
BIOSTATS 748 Applied Survival Analysis
Introduction to statistical techniques used for the analysis of time-to-event data. Types of censoring mechanisms, graphical and numerical description of survival data, methods for comparison of survival between groups, Cox and AFT models to explain and predict survival as a function of baseline and time-varying covariates.
BIOSTATS 749 Statistical Methods for Clinical Trials
Statistical techniques in the design, analysis and interpretation of clinical trials. Types of clinical research, study design, treatment allocation, randomization and stratification, quality control, sample size requirements, patient consent, introduction to survival analysis and interpretation.
BIOSTATS 750 Applied Statistical Learning
Introduce statistical modeling approaches, which have been developed in last few years and are widely used in medical and public health research, but are not covered in core courses of MS/PhD programs in Biostatistics and Epidemiology. Topics include penalized regression, methods for classification, evaluation of predicitions, and robust regression. . The cross-validation and bootstrapping procedures, which are important in evaluating and performing inference for models, will be introduced. The course emphasizes helping students to understand the concepts and ideas of some modern statistical methods and apply these methods to research medical and public health studies. Implementation of different methods with R software will be introduced whenever appropriate. Prerequisites: BIOSTATS 540 and BIOSTATS 640.
BIOSTATS 790A Advanced Statistical Inference
The course covers classical likelihood inference; an introduction to basic asymptotic analysis; and modern topics like M-estimation, the jackknife, and the bootstrap. Pre-Requisite: STATISTC 607 & 608.
BIOSTATS 790C Causal Inference Special Topic
This course will introduce students to both statistical theory and practice of causal inference. We will review the basics of causal inference, introduce a missing data perspective of causal inference and instrumental variable methods. We then cover 3 advanced topics based on a survey to students. Tentative topics include randomization inference, mediation analysis, principal stratification, measurement error, natural experiments, and causal inference with interference. Prerequisite: STATISTC 515 and 516.
796 Independent Study
797 Special Problems
891 Research Seminar
EPI 892A Doctoral Seminar in Epidemiology
Credit, 1.
EPI 892BW Advanced Epidemiological Methods Seminar
A PhD level seminar class that will explore complex and contemporary methodological concepts used in epidemiological research, as described in the published epi methods literature. Credit, 1. Prerequisites: EPI 737.
BIOSTATS 892D Doctoral Seminar in Biostatistics
Credit, 1
896 Doctoral Independent Study
Credit, 1-6.
899 Doctoral Dissertation
Credit, 18.