1 Week Courses
Personal Computers:
A laptop computer is essential and required for enrolled students (tablets or other
similar devices are also suitable). We recognize computers to be an extension of the
learning tools needed to successfully participate in our courses. Coursepacks for
most courses will be available digitally via the University's Canvas course learning
management system.
EPID 707 Nutritional Epidemiology
(1 credit hour) Eduardo Villamor
This course focuses on the design, conduct, analysis, and interpretation of epidemiologic
studies addressing diet and health. The course will review quantitative methodological
issues including dietary assessment methods, sources of variation in the diet, energy
intake, measurement error, anthropometry and body composition, and biomarkers of intake.
Students will advance their knowledge in nutrition research from a population perspective
and gain practical experience in the analysis and interpretation of real dietary data.
Prerequisites: Introductory course in epidemiology that covers study designs and measures
of disease frequency and association. Introductory course in biostatistics that covers
correlation and basic regression (e.g. linear and logistic regression). Knowledge
of nutrition is desirable but not required. Syllabus for Epid 707
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 clinical measurement and evaluative 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 recent medical literature pertaining to issues of causation,
diagnosis, management, and economic evaluation. The format will include problem-based
learning. 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.
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.
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 Epid 719
EPID 722 Medical Product Epidemiology and Global Regulation
(1 credit hour) Judith J.K. Jones
Almost all medical products- pharmaceuticals, biologics, vaccines, devices and other
medical products, are highly regulated worldwide. This has stimulated the need for
data and varied studies on very large populations to establish the safety of the products.
This course will cover the epidemiologic methods to study the use and effects of medical
products within the context of regulatory requirements for safety and appropriate
use. In particular, it will cover spontaneous reporting systems, ad hoc epidemiologic
studies and the growing use of large automated healthcare databases (big data). Emphasis
will be placed on the need to quantify the frequency of events in the different types
of products and the risk factors that predispose to them. We will also address the
more recent implementation of pharmaceutical risk management plans, and the implementation
of different safety monitoring systems by regulators such as the FDA and the European
Medicines Agency (EMA). Other topics include differences in epidemiology of the different
medical products and methods to measure the frequency of their use. The courses will
include lectures, group exercises to understand the stages from signal/hypothesis
to study protocol development, and will finish with participants' presentations to
a hypothetical regulator. Prerequisite: Introductory level course in epidemiology. Syllabus for Epid 722
EPID 742 Introduction to Generalized Linear Models
(1 credit hour) Hyungjin Myra Kim
The vast majority of epidemiologic data involve either binary or count data. Logistic
(binary data) and Poisson (count data) regression analyses are two important analytic
approaches that frequently provide valuable insights into collected data. Both techniques
will be 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 several multivariable epidemiologic data sets. These two analytic approaches
not only provide simple and effective ways to explore complex relationships but illustrate
the general process of using a linear model to draw conclusions from the analysis
of epidemiologic data. Prerequisites: Introductory level courses in epidemiology and
biostatistics.
EPID 747 Successful Scientific Writing
(1 credit hour) Paul Z. 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 expedite publication of their manuscripts. Working in small groups,
students spend much of their class time critiquing actual published and unpublished
manuscripts, including their own and solving a wide range of exercises that exemplify
the real-world challenges that authors face. Free-form in-class discussions make it
possible for class members to learn from one another's experiences. Major components
of the course include the following: basic sections of a scientific article: the purpose,
elements and organization of each section; principles of style for writing in public
health and epidemiology; systematic approaches to the process of writing and publishing
an article in a peer-review journal; and effective strategies for dealing with requests
of journal editors and reviewers. 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.
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. We will develop a basic understanding of how key
social factors shape the distribution of health and disease in the general population,
with a focus on the social gradients in health, health disparities across racial and
ethnic groups, and the role of the social environment in population health outcomes.
In addition, we will review a few key psychological and biological processes that
are thought to mediate or interact with social determinants in producing health differences
within and between populations. Finally, we will discuss the implications of the social
determinants of population health with regard to health and health care policies. Prerequisite:
Introductory level course in epidemiology.
EPID 762 Using STATA (or SAS) with Complex Survey Data to Examine Associations and
Time Trends
(1 credit hour) Donna Brogan
Many health surveys in the U.S. (e.g. NHANES, NHIS, BRFSS) and globally (e.g. DHS)
publicly release their data. Analysts who wish to make inference back to the target
population must use specialized statistical software that recognizes common and nonstandard
characteristics of these complex survey data.Course participants will conduct descriptive
and association analyses, trend analyses over time, and logistic regression analyses
with the statistical software package STATA using two complex survey datasets: the
2013 NHIS (National Health Interview Survey) and six years of BRFSS (Behavioral Risk
Factor Surveillance System) data for one state.For those with no or limited STATA
experience, a Sunday session will be held the day before the course begins to review
selected commands in STATA. A complimentary version of STATA can be downloaded to
each participant's computer for use during the course. Although instruction will focus
on STATA's capabilities for analysis of complex survey data, participants already
familiar with the SAS survey procedures may wish to conduct some of the course analyses
also in SAS.
Prerequisites: Introductory courses or experience in epidemiology, logistic regression,
and use of a statistical software package such as STATA, SAS or R for data management
and data analysis. Background in the theory and/or practice of selecting probability
samples is helpful but not required.Many health surveys in the U.S. (e.g. NHANES,
NHIS, BRFSS) and globally (e.g. DHS) publicly release their data. Analysts who wish
to make inference back to the target population must use specialized statistical software
that recognizes common and nonstandard characteristics of these complex survey data. 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.
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 (e.g., EPID 701).
EPID 780 Applied Epidemiologic Analysis for Causal Inference
(1 credit hour) Jay Kaufman
This course focuses on regression models of potential outcomes for the estimation
of causal parameters in epidemiologic research. One day of theoretical background
will be followed by 4 days of lectures on a range of techniques along with a computer
laboratory portion with hands-on exercises using the Stata statistical software package
(V13 or higher). Techniques covered include propensity score matching, inverse probability
weighting for confounder control and for censoring, marginal structural models, mediation,
the parametric g-formula, and some introduction to econometric techniques such as
differences-in-differences. Emphasis is on understanding the causal models, generating
analysis with software code, and interpreting the resulting estimates. Prerequisites:
Students should have at least one basic epidemiology course and some background in
Stata, along with a working knowledge of regression and other standard statistical
methodology common in basic epidemiological analysis.
EPID 783 Methods in Community-Based Participatory Research for Health
(1 credit hour) Barbara A. Israel, Wilma Brakefield-Caldwell, J. Ricardo Guzman, Edie
Kieffer, Toby Lewis, Gloria Palmisano, Angela Reyes, Zachary Rowe, Amy Schulz
(Co-taught by other faculty and community partners involved in the Detroit Community-Academic
Urban Research Center (http://www.detroiturc org/) and its affiliated CBPR projects)
There is increasing recognition and support for more comprehensive and participatory
approaches to research and interventions in order to address the complex set of determinants
associated with public health problems that affect populations generally, as well
as those factors associated with racial and ethnic disparities in health more specifically.
Community-based participatory research (CBPR) is one such partnership approach that
equitably involves all partners in all aspects of the research and intervention process,
aimed at both increasing knowledge and understanding and linking the knowledge gained
with interventions and policy change to enhance the health and quality of life of
community members.
This course will provide an introduction to some of the core principles, concepts and methods involved in using a CBPR approach. Organized along the phases of CBPR, this course will focus on describing and understanding partnership formation, maintenance, and evaluation; the use of quantitative and qualitative methods (e.g., survey, focus group interview, observational checklist) for the purposes of community assessment, examining basic research questions, and developing and evaluating interventions; and feedback, interpretation, dissemination and application of research results. The course will examine the rationale for, benefits of and challenges associated with using a community-academic partnership approach to research and interventions. Class format includes lectures, discussions, case studies, and small group exercises. No prerequisite. Syllabus for EPID 783
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: Introductory level course in statistics
(including an introduction to regression methods).
EPID 787 An Introduction to Multilevel Analysis in Public Health
(1 credit hour) Katherine M. Keyes
Multilevel analysis is an essential analytic tool in epidemiology and public health
that allows the simultaneous investigation of the effects of factors defined at multiple
levels on individual-level outcomes. This short course will review the rationale for
multilevel analysis in public health research, build the theory and practice of these
models from the fundamentals of the statistical approach and demonstrate a variety
of different forms that the models can take. Fitting and interpreting models will
demonstrate using Stata 13 statistical software. Special emphasis will be placed on
the strengths and limitations of multi-level analysis in investigating social and
group-level determinants of health. Prerequisites: Knowledge of basic epidemiology
and linear and logistic regression. Syllabus for EPID 787
EPID 793 Complex Systems Modeling for Public Health Research – FULL DAY COURSE
(2 credit hours) Kristen Hassmiller Lich
This course will provide an introduction to three major complex systems science modeling
techniques with wide applicability to public health. We will cover an introductory
overview of each technique, examples of applications, brief discussions of best practices,
and some initial hands-on lab experience. 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 and describe each of the three approaches and their respective advantages/disadvantages.
Students will be well positioned to further explore the incorporation of systems science
methods into their own research or participation in interdisciplinary teams using
the modeling techniques. Prerequisite: Introductory level course in epidemiology.
Syllabus for EPID 793
EPID 798 Epidemiologic Data Analysis using R
(1 credit hour) Sung Kyun Park
This course will introduce the R statistical programming language for epidemiologic
data analysis. R is freely available, versatile, and a powerful program for statistical
computing and graphics. This course will focus on core basics of organizing, managing,
and manipulating data; basic graphics in R; descriptive methods and regression models
widely used in epidemiology. Prerequisites: Introductory level courses in epidemiology
and biostatistics are required. Prior experience of R is not required. Syllabus for EPID 798
