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
(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. 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.
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 716 Clinical Epidemiology and Evidence-Based Research
(1 credit hour) Mitchell A.H. Levine
With the increasing demand for an evidence-based approach in the delivery of healthcare
services and the economic pressures for a more rational and efficient use of limited
healthcare resources, practitioners and administrators in the healthcare field need
to develop evaluation skills in order to conduct their work optimally. Clinical Epidemiology
and Evidence-Based Research identifies and teaches these skills. The course will cover
the basic concepts of clinical epidemiology in the context of appraising the medical
literature pertaining to issues of causation, diagnosis, management, and economic
evaluation. The format will be mix of didactic and interactive learning opportunities.
All health professionals (clinicians and administrators) who rely on the medical literature
to guide their activities are invited to attend the course. No prerequisite. Syllabus for EPID 716
EPID 717 Design and Conduct of Clinical Trials
(1 credit hour) Stephen J. Farish
The theoretical and practical challenges to be considered in designing and conducting
a randomized clinical trial will be presented. Topics to be discussed include the
specification of a primary objective, adherence to accepted ethical guidelines, the
role of randomization and the means of its implementation, the choice of design strategy
and design strengthening features, considerations involved in sample size determination
and patient recruitment and standards for reporting clinical trials. Detailed analytic
issues will be considered in the complementary one-week course that follows. Prerequisite:
Introductory course in epidemiology. Syllabus for EPID 717
EPID 718 Analysis of Clinical Trials
(1 credit hour) Stephen J. Farish
Methods of analysis appropriate to various designs, such as cross-over designs, nested
designs, factorial designs, and designs with repeated measures will be presented.
The use of GLM techniques for analysis will also be illustrated. Topics will include
estimation of survival functions, survival comparison between groups of subjects,
identification of important covariates, adjustment for covariates, testing for interaction,
and understanding the difference between confounding and interaction. Specific tools
to be discussed include the Kaplan-Meier estimators, the log-rank (Mantel-Haenszel)
statistics, and the Cox proportional hazards model. Instruction will focus on the
empirical use of methodologies rather than formal algebraic knowledge. Practical applications
of manual and software-based analysis will illustrate specific procedures and interpretation
of results. Students receive a disk with the data and analysis programs for all examples
in the course. Students are advised to bring a scientific calculator. Prerequisite:
Introductory course in biostatistics. Syllabus for EPID 718
EPID 719 Methods in Genetic and Epigenetic Epidemiology
(1 credit hour) Edward Ruiz-Narvaez
This course familiarizes students with general methods and principles of genetic and
epigenetic epidemiology. The course seeks to integrate concepts in human genetics,
population genetics, epidemiology, and biostatistics. The course will emphasize the
practical applications of existing methods, which requires a critical evaluation of
the scientific literature. Students are expected to be active participants in the
course. Some of the topics to be included are population genetics, genetics of common
diseases, gene-environment interaction, genetic and epigenetic association studies,
and social epigenomics. Prerequisites: Introductory level course in epidemiology that
covers study designs and measures of disease frequency and association. Introductory
level course in biostatistics that covers correlation and basic regression (e.g. linear
and logistic regression) Syllabus for EPID719
EPID 720 Applied Mediation Analysis
(1 credit hour) Linda Valeri
The course will approach concepts and methods for mediation from the perspective of
the counterfactual framework. Mediation analysis quantifies the extent to which the
effect of an exposure on some outcome is mediated through a particular intermediate
and the extent to which it is direct or through other pathways. Definitions, identification
results and statistical techniques related to mediation analysis will be covered.
The course will clarify the assumptions required for the estimation of direct and
indirect effect and will extend the approach to mediation typically employed in epidemiology
and the social sciences to settings with interactions, non-linearities, and time-varying
exposures. Prerequisite: Familiarity with regression analysis and potential outcomes. Syllabus for EPID 720
EPID 721 Applied Sensitivity Analyses in Epidemiology
(1 credit hour) Onyebuchi A. Arah
This course introduces how to think about and conduct sensitivity analyses for uncontrolled
confounding, selection bias and measurement error in epidemiologic studies. The course
will demonstrate the intuition behind the separate and combined consequences of these
sources of bias on estimating and inferring causal effects. It will provide practical
quantitative skills for assessing the sensitivity of analytical results to these biases
in order to aid credible causal modeling and inference using empirical epidemiologic
studies. Prerequisite: Introductory epidemiology. Introductory biostatistics or introduction
to generalized linear models. Working knowledge of a general statistical software
like SAS, Stata or R. An introductory course on causal inference (e.g. EPID 780) is
highly recommended. Syllabus for EPID 721
EPID 724 Leadership and Strategic Planning for Public Health
(1 credit hour) Laura Power
This course focuses on leadership skills and strategic planning for public health
and healthcare professionals with the ultimate goal of readying students for public
health 3.0. Students will learn approaches to empower teams and to collaborate across
sectors and will practice using systems thinking and policy evaluation as tools for
promoting health for individuals and populations. The course will include self-assessment
of leadership skills, practice in identifying appropriate leadership and management
techniques, and analysis of case studies to understand policy evaluation and systems
thinking. Students will be encouraged to bring real-world experience to the class
lessons and discussions. No prerequisite. Syllabus for EPID 724
EPID 730 Simulation Modeling of Tobacco Use, Health Effects and Policy Impacts
(2 credit hours) Rafael Meza and Jihyoun Jeon
This course will introduce students to the use of simulation modeling to assess the
burden of tobacco use on health, and project the impact of tobacco control interventions
and regulations on use patterns and downstream health effects. The course will provide
an overview of state transition and dynamical system models, their application in
public health and policy making, and the use of simulation modeling in tobacco control.
Students will learn about the main tobacco simulation models in the literature, become
familiar with state transition and dynamical system models, and develop and implement
their own smoking simulation models in the R statistical software or Excel. The course
will be a combination of lectures by leading experts in the field, modeling lectures,
and hands-on lab sessions. Prerequisite: Either tobacco epidemiology or tobacco control
knowledge, or familiarity with modeling. For those without modeling background, we
recommend taking Epid 793 Complex Systems Modeling course first. Students should have
basic statistical or epidemiology knowledge. Syllabus for EPID 730
EPID 742 Generalized Linear Models
(1 credit hour) Kevin (Zhi) He
This course will cover regression methods for continuous, binary, and count data.
The
majority of epidemiologic data involve either binary or count data, and binary data
often
arise from underlying continuous data. Therefore, linear (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 applied fashion. The presentation
will be heavily focused on R implementation with SAS equivalents provided as an
additional resource. The discussed material will begin with the simplest cases with
the
goal of understanding the fundamental properties of each model. Once these properties
are established, more advanced topics such as collinearity, variable selection, and
goodness-of-fit will be described and applied to example epidemiologic data sets.
Propensity matching and zero-inflated models will be introduced. The course will teach
simple and effective ways to explore complex relationships and illustrate the general
process of using a model to draw appropriate conclusions. Prerequisite: Introductory
level courses in epidemiology and biostatistics. Syllabus for EPID 742
EPID 747 Successful Scientific Writing
(1 credit hour) Paul Siegel, John Iskander
This course takes an active, participatory approach to help public health and health
care professionals learn how to communicate the findings of their research and investigations
more effectively and interact with journal editors and reviewers in ways that can
expedite publication of their manuscripts. Time will be spent working in small groups
to critique actual published and unpublished manuscripts and solving exercises that
exemplify the real-world challenges that authors face. No prerequisite. Syllabus for EPID 747
EPID 757 Systematic Reviews and Meta-analyses
(1 credit hour) Joel Gagnier
Systematic reviews and meta-analyses are useful for evidence-based clinical and public
health practice. The widespread and growing application of systematic review methods
for the synthesis of evidence on important or pressing research and clinical questions
underscore the need for health-care professionals to understand and critique this
research design. This course will provide a detailed description of the systematic
review process, discuss the strengths and limitations of the method, and provide step-by-step
guidance on how to actually perform a systematic review and meta-analysis. Specific
topics to be covered include formulation of the review question, searching of literature,
quality assessment of studies, data extraction, meta-analytic methods, assessment
of heterogeneity and report writing. The course will also cover statistical issues
such as selection of statistical models for meta-analysis, practical examples of fixed
and random effects models, best evidence syntheses (qualitative systematic reviews)
as well as examples of methods to evaluate heterogeneity and publication bias. STATA
statistical software will be used to perform meta-analysis during the computer lab,
along with tutorials on how to effectively use tools such as PubMed for conducting
reviews. Prerequisites: Introductory level courses in epidemiology and biostatistics
are recommended. Syllabus for EPID 757
EPID 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. Syllabus for EPID 780
EPID 787 Multilevel Analysis
(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
(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