1 Week Courses

Course materials will be available digitally via the University's Canvas course learning management system.

UMSSE classroom

EPID 703 Applied Infectious Disease Modeling
(I credit hour) Andrew Brouwer
Infectious disease modeling is increasingly being used to inform policy, practice, and research. This course will provide an introduction to the epidemiological and mathematical concepts underlying infectious disease modeling as well as the application these concepts through hands-on model implementation. This course will be taught in an alternating lecture and lab style; we will be coding in R software. We will discuss the use of models in making predictions, selecting interventions, and assessing counterfactuals. Student will develop skills identifying the important underlying processes and assumptions in the infectious disease systems they want to model.  We will explore the basic reproduction number, its importance to infectious disease dynamics, and how it is calculated. We will compare and contrast compartmental, stochastic, and agent-based model frameworks, as well as deterministic and stochastic model implementations. We will consider how models can be connected to data, introducing parameter identifiability, parameter estimation, and uncertainty quantification.Prerequisites: experience with modeling or good quantitative background, including statistics and differential equations; familiarity with R software. Syllabus for EPID 703

EPID 708 Machine Learning for Epidemiologic Analysis in the Era of Big Data
(1 credit hour) Alan Hubbard

The course focuses on advances in machine learning and its application to causal inference and prediction via a so-called Targeted Learning approach. These techniques allow the use of machine learning algorithms not just for prediction, but for estimating so-called causal parameters, such as average treatment effects, direct and indirect effects, dynamic treatments, optimal treatment regimes, etc. Targeted Learning provides the theoretical framework for deriving substitution estimators and rigorous statistical inference. Such techniques will become increasingly important in the era of Big Data, and the course will focus on the implementation of these techniques on existing data. The course will have a computer lab portion based on the R programming. Time permitting, we will discuss the implementation of approaches via cloud computing. This course is targeted towards more methods oriented epidemiologists and students. Prerequisites: Some background in R (can be obtained through EPID798 in this summer session) and working knowledge of regression and other standard statistical methodology common in basic epidemiological analysis. Syllabus for EPID 708

EPID 716 Clinical Epidemiology and Evidence-Based Research
(1 credit hour) Mitchell A.H. Levine
With the increasing demand for an evidence-based approach in the delivery of healthcare services and the economic pressures for a more rational and efficient use of limited healthcare resources, practitioners and administrators in the healthcare field need to develop evaluation skills in order to conduct their work optimally. Clinical Epidemiology and Evidence-Based Research identifies and teaches these skills. The course will cover the basic concepts of clinical epidemiology in the context of appraising the medical literature pertaining to issues of causation, diagnosis, management, and economic evaluation. The format will be mix of didactic and interactive learning opportunities. All health professionals (clinicians and administrators) who rely on the medical literature to guide their activities are invited to attend the course. No prerequisite. Syllabus for EPID 716

EPID 717 Design and Conduct of Clinical Trials 
(1 credit hour) Stephen J. Farish
The theoretical and practical challenges to be considered in designing and conducting a randomized clinical trial will be presented. Topics to be discussed include the specification of a primary objective, adherence to accepted ethical guidelines, the role of randomization and the means of its implementation, the choice of design strategy and design strengthening features, considerations involved in sample size determination and patient recruitment and standards for reporting clinical trials. Detailed analytic issues will be considered in the complementary one-week course that follows. Prerequisite: Introductory course in epidemiology. Syllabus for EPID 717

EPID 718 Analysis of Clinical Trials
(1 credit hour) Stephen J. Farish

Methods of analysis appropriate to various designs, such as cross-over designs, nested designs, factorial designs, and designs with repeated measures will be presented. The use of GLM techniques for analysis will also be illustrated. Topics will include estimation of survival functions, survival comparison between groups of subjects, identification of important covariates, adjustment for covariates, testing for interaction, and understanding the difference between confounding and interaction. Specific tools to be discussed include the Kaplan-Meier estimators, the log-rank (Mantel-Haenszel) statistics, and the Cox proportional hazards model. Instruction will focus on the empirical use of methodologies rather than formal algebraic knowledge. Practical applications of manual and software-based analysis will illustrate specific procedures and interpretation of results. Students receive a disk with the data and analysis programs for all examples in the course. Students are advised to bring a scientific calculator. Prerequisite: Introductory course in biostatistics. Syllabus for EPID 718 

EPID 719 Methods in Genetic and Epigenetic Epidemiology 
(1 credit hour) Edward Ruiz-Narvaez
This course familiarizes students with general methods and principles of genetic and epigenetic epidemiology. The course seeks to integrate concepts in human genetics, population genetics, epidemiology, and biostatistics. The course will emphasize the practical applications of existing methods, which requires a critical evaluation of the scientific literature. Students are expected to be active participants in the course. Some of the topics to be included are population genetics, genetics of common diseases, gene-environment interaction, genetic and epigenetic association studies, and social epigenomics. Prerequisites: Introductory level course in epidemiology that covers study designs and measures of disease frequency and association.  Introductory level course in biostatistics that covers correlation and basic regression (e.g. linear and logistic regression) Syllabus for EPID719

EPID 720 Applied Mediation Analysis
(1 credit hour) Linda Valeri
The course will approach concepts and methods for mediation from the perspective of the counterfactual framework. Mediation analysis quantifies the extent to which the effect of an exposure on some outcome is mediated through a particular intermediate and the extent to which it is direct or through other pathways. Definitions, identification results and statistical techniques related to mediation analysis will be covered. The course will clarify the assumptions required for the estimation of direct and indirect effect and will extend the approach to mediation typically employed in epidemiology and the social sciences to settings with interactions, non-linearities, and time-varying exposures. Prerequisite: Familiarity with regression analysis and potential outcomes. Syllabus for EPID 720

EPID 721 Applied Sensitivity Analyses in Epidemiology
(1 credit hour) Onyebuchi A. Arah
This course introduces how to think about and conduct sensitivity analyses for uncontrolled confounding, selection bias and measurement error in epidemiologic studies. The course will demonstrate the intuition behind the separate and combined consequences of these sources of bias on estimating and inferring causal effects. It will provide practical quantitative skills for assessing the sensitivity of analytical results to these biases in order to aid credible causal modeling and inference using empirical epidemiologic studies. Prerequisite: Introductory epidemiology. Introductory biostatistics or introduction to generalized linear models. Working knowledge of a general statistical software like SAS, Stata or R. An introductory course on causal inference (e.g. EPID 780) is highly recommended. Syllabus for EPID 721

EPID 724 Leadership and Strategic Planning for Public Health
(1 credit hour) Laura Power
This course focuses on leadership skills and strategic planning for public health and healthcare professionals with the ultimate goal of readying students for public health 3.0. Students will learn approaches to empower teams and to collaborate across sectors and will practice using systems thinking and policy evaluation as tools for promoting health for individuals and populations. The course will include self-assessment of leadership skills, practice in identifying appropriate leadership and management techniques, and analysis of case studies to understand policy evaluation and systems thinking. Students will be encouraged to bring real-world experience to the class lessons and discussions. No prerequisite. Syllabus for EPID 724

EPID 730 Simulation Modeling of Tobacco Use, Health Effects and Policy Impacts
(2 credit hours) Rafael Meza and Jihyoun Jeon
This course will introduce students to the use of simulation modeling to assess the burden of tobacco use on health, and project the impact of tobacco control interventions and regulations on use patterns and downstream health effects. The course will provide an overview of state transition and dynamical system models, their application in public health and policy making, and the use of simulation modeling in tobacco control. Students will learn about the main tobacco simulation models in the literature, become familiar with state transition and dynamical system models, and develop and implement their own smoking simulation models in the R statistical software or Excel. The course will be a combination of lectures by leading experts in the field, modeling lectures, and hands-on lab sessions. Prerequisite:  Either tobacco epidemiology or tobacco control knowledge, or familiarity with modeling. For those without modeling background, we recommend taking Epid 793 Complex Systems Modeling course first. Students should have basic statistical or epidemiology knowledge. Syllabus for EPID 730

EPID 742 Introduction to Linear, Logistic and Poisson Regression
(1 credit hour) Ananda Sen
This course will cover regression methods for continuous, binary, and count data. Majority of epidemiologic data involve either binary or count data, and binary data often arise from an underlying continuous data. Therefore, multiple (for continuous data), logistic (for binary data) and Poisson (for count data) regression analyses are all important analytic approaches that frequently provide valuable insights into data collected for epidemiologic studies. All approaches will be covered under the umbrella of generalized linear models (GLM) and presented in a practical and an applied fashion. The discussed material begins with the simplest case with the goal of understanding the fundamental properties of each model. Once these properties are established, more advanced topics such as collinearity, variable selection, non-linear explanatory variables, and goodness-of-fit will be described and applied to example epidemiologic data sets. These approaches provide simple and effective ways to explore complex relationships and illustrate the general process of using a model to draw appropriate conclusions. Prerequisite: Introductory level courses in epidemiology and biostatistics. Syllabus for EPID 742

EPID 747 Successful Scientific Writing
(1 credit hour) Paul Siegel,  John Iskander
This course takes an active, participatory approach to help public health and health care professionals learn how to communicate the findings of their research and investigations more effectively and interact with journal editors and reviewers in ways that can expedite publication of their manuscripts. Time will be spent working in small groups to critique actual published and unpublished manuscripts and solving exercises that exemplify the real-world challenges that authors face. No prerequisite. Syllabus for EPID 747

EPID 757 Systematic Reviews and Meta-analyses
(1 credit hour) Joel Gagnier
Systematic reviews and meta-analyses are useful for evidence-based clinical and public health practice. The widespread and growing application of systematic review methods for the synthesis of evidence on important or pressing research and clinical questions underscore the need for health-care professionals to understand and critique this research design. This course will provide a detailed description of the systematic review process, discuss the strengths and limitations of the method, and provide step-by-step guidance on how to actually perform a systematic review and meta-analysis. Specific topics to be covered include formulation of the review question, searching of literature, quality assessment of studies, data extraction, meta-analytic methods, assessment of heterogeneity and report writing. The course will also cover statistical issues such as selection of statistical models for meta-analysis, practical examples of fixed and random effects models, best evidence syntheses (qualitative systematic reviews) as well as examples of methods to evaluate heterogeneity and publication bias. STATA statistical software will be used to perform meta-analysis during the computer lab, along with tutorials on how to effectively use tools such as PubMed for conducting reviews. Prerequisites: Introductory level courses in epidemiology and biostatistics are recommended. Syllabus for EPID 757

EPID 761 Social Determinants of Population Health
(1 credit hour) Carlos Mendes de Leon
This course will provide an introduction to current concepts and research in the social determinants of population health. The primary goal of the course is to develop a basic understanding of how key social determinants shape the distribution of health and disease in the general population, with a focus on the social gradients in health and health disparities across racial and ethnic groups. The course will also cover basic individual-level and community-level mechanisms by which social determinants affect population health outcomes. Lastly, the course will discuss how social determinants are increasingly incorporated in population health and health care policies. Prerequisites: Background training or experience in any health-related field is recommended. Syllabus for EPID 761

EPID 762 Analysis of Complex Sample Survey Data
(1 Credit hour) Brady T. West
This course will present a practical overview of modern techniques for analyzing survey data in a way that accounts for the complex features of the sample design that gave rise to the sample of units that was ultimately surveyed. Examples of such complex sample design features include unequal probabilities of selection for different units (e.g., oversampling of subgroups), leading to a need to use weights in estimation; nonresponse adjustment of sampling weights to compensate for survey nonresponse; allocation of the sample to different sampling strata, to increase the efficiency of estimates; and cluster sampling, to reduce the costs of data collection. A failure to account for weights in estimation can lead to biased estimates of population quantities, and a failure to account for sampling features like stratification and cluster sampling in estimating standard errors can lead to biased population inferences. After providing an overview of critical concepts related to complex sampling, estimation, and variance estimation, the course will focus on practical applications of analysis techniques using existing commands in the Stata software (V16). Overall, this course is designed to reduce the prevalence of analytic error in epidemiological studies employing secondary analyses of complex sample survey data.
Prerequisites: Students should have basic familiarity with statistical sampling concepts, including stratification, cluster sampling, and weighting, as well as basic statistical concepts, including point estimation, sampling variance, confidence intervals, p-values, the maximum likelihood estimation method and simple linear and logistic regression models. Familiarity with using statistical software for data analysis is also required. Syllabus for EPID 762

EPID 766 Analysis of Longitudinal Data from Epidemiologic Studies
(1 credit hour) Daowen Zhang
It has been popular in epidemiology to conduct longitudinal studies where study participants are followed over time and repeated measurements of interest are obtained. Compared to traditional cross-sectional or case-control studies, longitudinal studies can be more efficient to detect a difference of interest, offer more evidence for possible causal inference, etc. However, longitudinal data are likely to be correlated, which presents a substantial challenge in analyzing such data. This course will address 1) epidemiologic methods for the design and interpretation of longitudinal studies involving repeated measures and 2) statistical methods appropriate for longitudinal data including generalized estimating equations (GEEs), linear mixed models and generalized linear mixed models. A series of studies will be used to illustrate the major design issues and statistical approaches. Relevant procedures in statistical package SAS will be introduced and appropriate interpretation of results will be emphasized. Prerequisites: Students are expected to have one or two graduate biostatistics courses on (simple and multiple) linear regression models, categorical data analysis such as logistic regression models and experience of conducting data analysis using statistical software SAS. Syllabus for EPID 766

EPID 777 Geographic Information Systems for Epidemiology
(1 credit hour) Shannon J. Brines
Geographic Information Systems (GIS) are used for displaying and analyzing spatial data. Data from a variety of sources may be compared utilizing overlay analysis and spatial statistics. Modern tools permit novice GIS users to perform spatial analysis without extensive training. This course will introduce students to ArcGIS, the world's leading GIS analysis package. Examples of epidemiological applications will give students the opportunity to see and use this powerful tool. Some of the topics to be covered are data import/export, layering, data table management, classification, labeling, spatial and attribute queries, and buffer analysis. No prerequisite. Syllabus for EPID 777

EPID 778 Spatial Statistics for Epidemiological Data
(1 credit hour) Veronica Berrocal

With the increasing availability of geographic information systems, spatial data have become more frequent in many disciplines, including public health and epidemiology. This course aims to provide an introduction to spatial statistical methods for epidemiological data, covering modeling approaches for the two different types of spatial data: point-referenced data, where the geographical coordinates of the observations have been recorded; and areal-averaged data, where summary statistics (e.g., number of disease cases by county, zip code, etc.) are reported for each areal unit. Topics covered include exploratory analysis for spatial data, covariance functions, kriging, spatial regression; disease mapping, spatial smoothing; point processes, assessment of clustering, and cluster detection. Each lecture will feature a lab component, during which spatial analyses of datasets, made available to the participants, will be performed using the publically available R statistical software (downloadable to your laptops at www.r-project.org). Although previous experience with R is preferred, it is not required. Prerequisite: Course in basic statistics and (e.g., EPID 701) and an introductory course in epidemiology (e.g. EPID 709). Syllabus for EPID 778

EPID 780 Applied Epidemiologic Analysis for Causal Inference
(1 credit hour) Jay Kaufman

This course focuses on concepts and application of potential outcomes for the

estimation of causal parameters in epidemiologic research. A thorough theoretical
background will be followed by a review of a range of techniques including directed
acyclic graphs, propensity score matching, inverse probability weighting for confounder control and for censoring, marginal structural models, the parametric g-formula, and common econometric techniques such as differences-in-differences.  Emphasis is on forming well-defined causal questions, and then understanding the causal models, their assumptions and identification requirements, and the valid interpretation of the estimated effect parameters.  To make the course more broadly accessible, I will try to avoid specific software packages, so no specific software background is required.  Prerequisites: students should have at least one basic epidemiology course with a working knowledge of regression and other standard statistical methodology common in basic epidemiological analysis. Syllabus for EPID 780

EPID 784 Survival Analysis Applied to Epidemiologic and Medical Data
(1 credit hour) Chris Andrews
The primary objective of this course is to provide participants with the background required to understand commonly used survival analysis methods and to apply such methods using standard statistical software.  The course material relies heavily on examples and intuitive explanations of concepts.  The mathematical level is completely accessible with knowledge of high school algebra, one semester of calculus, and a one-year course in basic statistical methods. Examples will be chosen from various epidemiologic and medical applications.  The topics will include: an introduction to survival analysis; right censoring and left truncation; life tables, non-parametric estimators (e.g., Kaplan-Meier, Nelson-Aalen); two- and k-sample tests (e.g., log-rank, Wilcoxon); parametric methods for analyzing survival data (e.g., exponential model); semi-parametric methods (e.g., Cox proportional hazards model).  The statistical techniques will be illustrated using various medical and epidemiological studies. Students will carry out some applied (pencil-and-paper) problems to illustrate the main ideas of survival analysis and to solidify the concepts. There will also be a number of data analysis exercises that will utilize statistical software.  Prerequisite: an Introductory level course in statistics (including an introduction to regression methods). Syllabus for EPID 784

EPID 793 Complex Systems Modeling for Public Health Research
(2 credit hours) Marisa Eisenberg, Michael Hayashi
This course will provide an introduction to two major complex systems science modeling techniques with wide applicability to public health. We will cover an introductory overview of complex systems modeling in general, and systems dynamics and agent-based modeling in particular. We will discuss model applications, best practices, and more advanced practical topics such as team-building, computation, funding, and publication. We will provide extensive hands-on lab experience during each section of the course. At the completion of the course the student will be able to explain current and potential future roles of complex systems science in public health, describe the respective advantages/disadvantages of each method covered, and will be expected to produce a draft proposal for applying one of the two system science methods to a particular problem. Students will become informed consumers of complex systems research, will be prepared to actively participate in interdisciplinary teams using the modeling techniques, and will be well positioned to incorporate systems science methods into their own research. Prerequisite: Relevant background in public health. Syllabus for EPID 793