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. Course materials
for most courses will be available digitally via the University's Canvas course learning
management system.
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 712 Epidemiology of Oral Diseases and their Role in General Health
(1 credit hour) Wenche Borgnakke
Participants in this course will learn about the measurement and epidemiology of oral
diseases and their extensive impact on quality of life. The course will illustrate
the use of epidemiologic approaches to describe oral manifestations of systemic diseases
and systemic manifestations of oral diseases as well as the financial and human costs
of dental care. The need for patient-centered, interprofessional collaboration approaches
will be discussed. The course will emphasize the evaluation of population and clinical
scientific evidence in oral health epidemiology. No prerequisite. Syllabus for EPID 712
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.
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 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.
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 the 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 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 742 Introduction to Linear, Logistic and Poisson Regression
(1 credit hour) Hyungjin Myra Kim
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) 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. 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. In addition, the course will
cover some of the basic individual-level and community-level mechanisms by which social
determinants affect population health outcomes. Finally, the course will review and
discuss the implications of the role of social determinants in population health for
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 Health 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;
non-response 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 (V15). Overall, this course is designed
to reduce the prevalence of analytic error in epidemiological studies employing secondary
analyses of complex sample survey data. Prerequisites: proficiency in basic mathematics,
including algebra and functions, is essential. Knowledge of calculus and linear algebra
is useful but not required for the course. A first course in survey sampling or research
methods and a basic understanding of sampling concepts such as stratification, cluster
sampling, and weighting is required.
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
(2 credit hours) 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 in the afternoons comprised of 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, 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. 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 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 SAS statistical software and code will be made available for Stata
and R programs as well. 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 78
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 powerful program for statistical
computing and graphics. This course will focus on the 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
