1 Week Courses
Course materials will be available digitally via the University's Canvas course learning management system.
EPID 702 Analysis with Missing Data in Epidemiology - ONLINE
(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 - ONLINE
(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 - ONLINE
(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 - ONLINE
(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 716 Clinical Epidemiology and Evidence-Based Research - IN PERSON/HYBRID
(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 - IN PERSON/HYBRID
(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 720 Applied Mediation Analysis - ONLINE
(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 - ONLINE
(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 - ONLINE
(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 - ONLINE
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 EPID730 (and EPID793 in conjunction
with EPID730). Apply for funding by April 14th. View more details
Syllabus for EPID 730
EPID 731 Analysis of Electronic Health Record (EHR) Data - ONLINE
(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 - ONLINE
(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 - IN PERSON/HYBRID
(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 - ONLINE
(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 Health - IN PERSON/HYBRID
(1 credit hour) Carlos Mendes de Leon
This course covers the major social determinants of health, and how these determinants
shape the distribution of health and disease in the general population. It is based
on the medical and public health literature on this topic, with specific emphasis
on social and racial/ethnic inequities, on individual and neighborhood-level social
characteristics in population health, and on the role of social determinants in health
care and health care policy Syllabus for EPID 761
EPID 766 Analysis of Longitudinal Data from Epidemiologic Studies - IN PERSON/HYBRID
(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 - ONLINE
(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 - IN PERSON/HYBRID
(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 - ONLINE
(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 - IN PERSON/HYBRID
(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 - IN PERSON/HYBRID
(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 - ONLINE
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. Syllabus for EPID 793