1 Week Courses

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

UMSSE classroom

EPID 702 Analysis with Missing Data in Epidemiology
(1 credit hour) Lu Wang
This course discusses both statistical theory and methodology aimed at addressing missing data problems in epidemiology studies. We will introduce different patterns of missing data, various missing data mechanisms, different statistical methods to deal with missing data, the advantages and disadvantages of each method, likelihood-based inference, data augmentation, multiple imputation, various missing patterns in longitudinal studies, drop-out, selection model, and pattern-mixture model. Overall, this course covers both applied and theoretical aspects related to statistical analysis with missing data. Prerequisites: introduction to statistical inferences, such as likelihood estimations; regression models; correlated and longitudinal data analysis.  Syllabus for EPID 702

EPID 703 Applied Infectious Disease Modeling
(1 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: Epid 793 is a good introduction to Epid 703 and Epid 730. Experience with modeling or good quantitative background, including statistics and differential equations; familiarity with R software. Syllabus for EPID 703

EPID 706 Mixed Methods in Epidemiologic Research
(1 credit hour) Timothy Guetterman
The intent of this course is to provide an overview of mixed methods research to learners who would like to gain familiarity with integrating qualitative research with quantitative research they may be familiar with. Participants will gain knowledge of the foundations of mixed methods research, mixed methods quality criteria, major mixed methods research designs, the value added of mixed methods research, and legitimation and validation concepts. Through an interactive, problem-based approach, attendees will develop skills in designing a mixed methods study throughout the course. The process of designing a study includes writing the introduction; explaining the justification for conducting mixed methods; developing a purpose statement and research questions; identifying a design; describing data sources and procedures for sampling, collection, and analysis; and the mixed methods integration of qualitative and quantitative data. In addition to designing a study, students will receive hands-on experience in conducting integrative, mixed methods analysis with datasets provided using MAXQDA software. Finally, we will introduce strategies for writing and reporting mixed methods research.   Prerequisites:  An introduction to quantitative research such as a course on statistics, epidemiology, research design or equivalent experience is helpful. Syllabus for EPID 706

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 and working knowledge of regression and other standard statistical methodology common in basic epidemiological analysis. Syllabus for EPID 708

EPID 712 Epidemiologic Foundations and Applications in Dental, Oral, and Craniofacial Diseases
(1 credit hour)  Kimon Divaris
The course will introduce students to the fundamentals of oral and dental epidemiology and will cover both substantive and methodological topics, as well as practical applications. There exists a long track record of epidemiologic inquiry informing the population distribution of dental, oral, and craniofacial diseases and conditions, their evolution of time, and their multi-level determinants. Instructional modules will emphasize that at the core of oral epidemiology lies the scientific approach to asking and answering oral health-relevant questions; important issues related to data collection; and recent developments in state-of-the-art analysis methods. Students will become familiar with foundational elements in the practice of oral epidemiology including key features of most common DOC diseases and conditions, study design, measurement and data collection, analysis methods, results interpretation and communication. Particular emphasis will be placed on epidemiologic studies of common oral and dental diseases (i.e., dental caries, periodontal diseases, and cancers of the head and neck), including measurement and data analysis issues that are specific to each condition.  No prerequisite.  Syllabus for EPID 712

EPID 716 Clinical Epidemiology and Evidence-Based Research
(1 credit hour) Caroline A. Thompson
With the increasing demand for evidence-based approaches to the delivery of healthcare services and the economic pressures for more rational and efficient use of limited healthcare resources, practitioners and administrators in the healthcare field need to develop evaluation skills 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 and risk, screening and diagnosis, prognosis, treatment and management, and economic evaluation. The format will be a 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 Analysis of Clinical Trials
(1 credit hour)
Matthew Schipper
This course will focus on the design and analysis of clinical trials.  We will cover a wide range of designs including Phase I, II and III trials.  For each phase of clinical trial we will cover the major designs and discuss the pros and cons of each.  Specific examples of topics include early phase adaptive dose finding designs, Bayesian phase II trials, non-inferiority trials and recent developments integrating real world data with clinical trials via external control arms. Students will learn how to design clinical trials including how to select sample size and how to develop interim stopping rules for safety and futility. We will also cover analysis considerations (e.g.  handling missing data and adjustment of significance for multiple hypotheses) and the CONSORT statement for reporting clinical trials. Prerequisites:  Introductory course in statistics/biostatistics/epidemiology with some basic familiarity with hypothesis testing. Syllabus for EPID 717

EPID 719 Bioinformatics Analysis of Epigenomics Data
(1 credit hour) Edward Ruiz-Narvaez
DNA methylation (DNAm) is an epigenetic modification that regulates gene expression and therefore, it may affect health outcomes. The major goal of this course is to provide students with bioinformatics tools to analyze and interpret epigenomics data in the setting of epidemiological studies. Students will apply these tools to an actual dataset containing DNAm, covariates, and outcome data. Some of the topics to be included are epigenome-wide association studies (EWAS), differentially methylated region (DMR) analysis, and estimation of different epigenetic clocks. Data management and analyses will be carried out in R. Prerequisites: Introductory courses in epidemiology and biostatistics. Syllabus for EPID 719

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 to lead from where they are. Students will learn approaches to empower teams and to collaborate across sectors and will practice using systems thinking and explore concepts of collective and adaptive leadership. The course will include self-assessments, reflection, and group discussion. 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
Full Day Course 

(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. The Center for the Assessment of Tobacco Regulations (CAsToR) is offering a limited number of scholarships for EPID 730 (and EPID 793 in conjunction with EPID730). Apply for funding by April 26th. View more details
Syllabus for EPID 730

EPID 731 Analysis of Electronic Health Record (EHR) Data
(1 credit hour) Xu Shi, Lars G. Fritsche  
This short course will offer an overview of modern analytical methods and research applications using EHR data, with a specific focus on epidemiologic inferences. Upon completion of the course, participants will i) gain knowledge of the structure and quality of EHR data, including data types, record generation process, data harmonization, and methods for extracting variables of interest, ii) attain a broader understanding of the opportunities and challenges of the secondary use of EHR data for research purposes, with a focus on epidemiologic principles including the role of study design, sources of bias, and generalizability, iii) explore and gain hands-on experience using EHRs from Michigan Medicine, and iv) be prepared to generate and further explore new questions and perspectives. Prerequisites: Quantitative training and working knowledge of R. The course will be instructed with minimal mathematics formulas and will include comprehensive examples to facilitate a broad and deep understanding of the topics and literature. Syllabus for EPID 731

EPID 733 Quasi-experimental Methods in Epidemiology
(1 credit hour) Zhehui Luo
This short course will introduce three useful tools that are becoming part of the repertoire of methods employed by epidemiologists: 1) instrumental variable analysis, 2) difference-in-differences methods, and 3) regression discontinuity design. These methods deal with unmeasured confounding in different ways. The course will cover the concepts, assumptions, statistical techniques, and empirical applications of these methods in the literature. Upon completion of the course, students will be able to critique the quality of a research paper that uses these methods and be able to conduct basic analyses in Stata or R. Prerequisites: Working knowledge of the counterfactual framework for causal inference and the directed acyclic graphs; familiarity with introductory epidemiology (e.g., confounding), and introductory biostatistics (e.g., expectation, laws of probability, linear regression); and some background in either Stata or R. Syllabus for EPID 733

EPID 747 Successful Scientific Writing
(1 credit hour) 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) Russell De Souza
Systematic reviews and meta-analyses form the basis for clinical care and public health
guidelines. They are also useful for researchers looking to understand the state of the research in a
given field. The widespread and growing application of systematic review methods for the synthesis ofevidence on important or pressing research and clinical questions underscore the need for health-careprofessionals 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, andprovide step-by-step guidance on how to perform a systematic review and meta-analysis. Specific topicsto be covered include formulation of the review question, searching of literature, quality assessment ofstudies, 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, as well as examples of methods to evaluate heterogeneity and publication bias. STATA statistical software and Review Manager will be used to perform meta-analysis during the computer lab, along with tutorials on how to effectively use tools suchas PubMed for conducting reviews. Prerequisites: Basic courses in epidemiology and biostatistics. Syllabus for EPID 757

EPID 761 Social Determinants of Population Health
(1 credit hour) Seungmi Yang
This course will introduce how society and various forms of social organizations shape and affect the distribution of health. It will cover conceptual and empirical social determinants of health literature to provide basic substantive knowledge on differential patterns of health across individual-level and contextual-level social factors. The course will also discuss methodological approaches to systematic assessment of health disparities across social groups including conceptualizing and measuring social exposures, monitoring and decomposing health disparity, examining contextual factors, and estimating policy impacts on health. The course format will be a combination of lectures and class discussions. Prerequisite:  Introductory courses in epidemiology/health science and statistics are recommended. Syllabus for EPID 761

EPID 766 Analysis of Longitudinal Data from Epidemiologic Studies
(1 credit hour) Ananda Sen
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) Catherine Lesko
This course introduces concepts and applications of potential outcomes and structural causal models for the estimation of causal parameters in epidemiologic research. The course will familiarize students with the assumptions underpinning modern causal inference methods and provide a conceptual understanding of standardization/g-computation  and inverse probability weighting. Students will apply each of these methods to estimating the effect of a time-fixed exposure in a simple setting. The course will also discuss the application of these methods in the literature. Prerequisites: Students should have at least one intermediate epidemiology course with a working knowledge of regression and other standard statistical methods in basic epidemiologic analysis (e.g., calculating risk differences, risk ratios, and odds ratios from 2x2 tables; knowing basic concepts of survival analyses like time origin, and how to read a survival curve; regression modeling for continuous and dichotomous response variables). Capacity in a standard software package such as R, SAS, or Stata is strongly advised although not enforced. Syllabus for EPID 780

EPID 784 Survival Analysis Applied to Epidemiologic and Medical Data
(1 credit hour) Kevin He 
The primary objectives of this course are 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 have been chosen from various epidemiologic and medical
applications. The course topics include: an introduction to survival analysis;
censoring and truncation; non-parametric (e.g. Kaplan-Meier estimator),
parametric (e.g., exponential model), and semi-parametric (e.g., Cox
proportional hazards model) estimators; two- and k-sample tests (e.g., log rank);
time-varying covariates; competing risks analysis; and recurrent event analysis.
Students will carry out some applied problems to illustrate the main ideas of
survival analysis and to solidify the concepts. In the labs, students will perform
more complex analyses with statistical software. Syllabus for EPID 784

EPID 787 An Introduction to Multilevel Analysis in Public Health
(1 credit hour) Jay Kaufman
Multilevel analysis is an essential analytic tool in epidemiology and public health that allows the simultaneous investigation of the effects of exposures defined at multiple levels on individual-level outcomes. This short course will review the rationale for multilevel analysis in public health research, build the statistical theory and practice of these models from the fundamentals of the regression-based approaches and demonstrate a variety of different forms that the models can take, including fixed and random effects, marginal (population average) models and extensions for categorical and survival outcomes.  Fitting and interpreting models will be demonstrated using Stata statistical software, and parallel code will also be provided in SAS. Special emphasis will be placed on the strengths and limitations of multilevel analyses in investigating social and group-level determinants of health, and the causal interpretations of estimated parameters.  Prerequisite:  Introductory course in epidemiology and an introductory course in statistics (i.e. some familiarity with regression modeling). Syllabus for EPID 787

EPID 793 Complex Systems Modeling for Public Health Research
Full Day Course

(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. The Center for the Assessment of Tobacco Regulations (CAsToR) is offering a limited number of scholarships for EPID793 in conjunction with EPID730. Apply for funding by April 26th. View more details 
Syllabus for EPID 793