Epidemiology Summer Term Courses

EPID562 Advanced Bacteriology Laboratory

  • Graduate Level
  • Fall, Winter, Spring, Summer term(s)
  • 2-6 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Winter 2016
  • Prerequisites: EPID 560 and EPID 561 or Perm. Instr.I
  • Description: Individual laboratory studies of selected topics on bacteria of public health importance. May be elected more than once.

EPID565 Research in Hospital and Molecular Epidemiology

  • Graduate Level
  • Fall, Winter, Spring, Summer term(s)
  • 1-6 Credit Hour(s)
  • Instructor(s): Staff
  • Offered every year
  • Last offered Winter 2016
  • Prerequisites: Perm. Instr.
  • Description: Investigation of a selected problem planned and carried out by each student. Pertinent literature, investigational approaches, and progress in the investigations are discussed in seminars. May be taken more than once for up to six credits. Usually taken first for one credit. This is the Capstone Course for Hospital and Molecular Epidemiology Students.

EPID604 Applications of Epidemiology

  • Graduate Level
  • Fall, Winter, Spring, Spring-Summer, Summer term(s)
  • 1-4 Credit Hour(s)
  • Instructor(s): Staff
  • Prerequisites: Instructor Permission
  • Description: Application of epidemiological methods and concepts to analysis of data from epidemiological, clinical or laboratory studies. Introduction to independent research and scientific writing under faculty guidance.
  • Syllabus for EPID604

EPID624 Readings in Epidemiology

  • Graduate Level
  • Fall, Winter, Spring, Summer term(s)
  • 1-2 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Winter 2016
  • Prerequisites: Perm. Instr.
  • Description: Review of literature on selected subjects under guidance of individual faculty members and through scheduled seminars at which reports are presented. May be elected more than once.

EPID702 Bayesian Perspectives in Epidemiology

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Staff; Little, Roderick;
  • Last offered Summer 2016
  • Prerequisites: courses in basic statistics and standard regression
  • Description: This course provides an introduction to Bayesian methods in epidemiology. Topics include: contrasting the Bayesian and classical approaches to hypothesis testing and interval estimation; strengths and weaknesses of the two paradigms, and when they give similar and dissimilar answers; objective and subjective Bayes; calibrated Bayes, a conceptual approach that combines Bayesian and frequentist ideas; computational tools, including Markov Chain Monte Carlo. the Bayesian approach to some important problems in epidemiology: contingency tables, diagnostic testing, comparison of means, regression, hierarchical models, measurement error, and analysis of data from common study designs.
  • Course Goals: The Bayesian approach to some important problems in epidemiology: contingency tables, diagnostic testing, comparison of means, regression, hierarchical models, measurement error, and analysis of data from common study designs.

EPID706 MEASUREMENT IN CLINICAL RESEARCH

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Gagnier, Joel
  • Last offered Summer 2016
  • Prerequisites: Introductory courses in epidemiology and biostatistics
  • Description: This course will provide an introduction to test/scale development theories (Classical Test Theory vs Item Response Theory) and the properties of clinical outcome measurement tools (i.e. validity, reliability and responsiveness).
  • Course Goals: To provide a comprehensive overview to attendees on the development, implementation and assessment of measurement tools, in particular patient reported outcome measures, for us in clinical research or practice. ,.
  • Competencies: The students that have taken this class are expected to be able to: C. 1. Describe health measurement test development theories C. 2. Comprehend key psychometric components of measurement tools for clinical research C. 3. Apply the basic terminology and definitions of measurement. C. 4. Comprehend and critically review measurement characteristics: validity, reliability, responsiveness and interpretability. ,.

EPID707 Nutritional Epidemiology

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Villamor, Eduardo
  • Prerequisites: EPID 701 or EPID 503 or EPID 600 or EPID 601 AND EPID 709 or BIOSTAT 501 or BIOSTAT 521
  • Description: This course focuses on the design, analysis, and interpretation of epidemiologic studies addressing diet and health. The course will provide quantitative practical skills to deal with methodological issues around dietary assessment methods, sources of variation in the diet, energy intake, measurement error, anthropometry and body composition, and biomarkers of intake.

EPID708 Machine Learning for Epidemiologic Analysis in the Era of Big Data

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Summer 2016
  • Description: Course focuses on advances in machine learning and its application to causal inference and prediction via Targeted Learning, which allows the use of machine learning algorithms for prediction and estimating so-called causal parameters, such as average treatment effects, optimal treatment regimes, etc. We will discuss implementation via cloud computing.
  • Course Goals: -A basic understanding of causal inference, including structural causal models, definition of causal parameters via counterfactual distributions, and ways to establish identifiability from observed data. -Familiarity and ability to implement machine learning, specifically the concepts of SuperLearning and the power of cross-validation in data-adaptive estimation. -Ability to apply machine learning algorithms to prediction problems and estimate and derive inference for the resulting fit. -Ability to use the fits of machine learning algorithms to estimate causal effects using simple substitution estimators. -Ability to apply Targeted Learning approaches (e.g., targeted maximum likelihood estimation) to estimate, using machine learning, a priori specified treatment effects as well as general variable importance measures. -A basic understanding of how to use parallel computing and large computer clusters to be able to estimate using computer intensive algorithms on large (Big Data) data sets. -How the general methodology applies to goals of Precision Medicine.
  • Competencies: -Ability to apply estimation roadmap to novel data questions. -Ability to implement estimation via R and existing software packages. -Basic knowledge of how to use such algorithms on Big Data including the use of cloud computing.

EPID712 DIABETES EPIDEMIOLOGY

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Herman, William
  • Undergraduates are allowed to enroll in this course.
  • Description: This course will focus on diabetes epidemiology through lecture and discussions. Topics will include diagnosis and classification, risk factors and projections of disease burden, screening for diabetes, the evidence-base for diabetes prevention,and landmark observational studies and clinical trials evaluating the impact of treatment on complications.
  • Course Goals: The student will understand issues related to: - diagnosis and classification - risk factors and current and future disease burden - screening for and prevention of type 2 diabetes - microvascular and neuropathic complications of diabetes - cardiovascular (CV) risk factors and CV disease in diabetes - treatment of diabetes, its complications, and comorbidities - translating interventions into community practice - diabetes computer simulation modeling
  • Competencies: The student will learn: - methodological issues related to diagnosing a chronic disease - why, when, who, where, and how to screen for diabetes - the advantages and disadvantages of primary, secondary, and tertiary interventions for diabetes - the challenges of conducting randomized controlled clinical trials in diabetes - the barriers to translating knowledge from clinical trials into community practice - the rationale for and limitations to chronic disease computer simulation modeling
  • Learning Objectives: The student will learn methodological issues related to diagnosing a chronic disease. Why, when, who, where, and how to screen for diabetes, the advantages, and disadvantages of primary, secondary, and tertiary interventions for diabetes and the challenges of conducting randomized controlled clinical trials in diabetes.

EPID719 Quantitative Methods in Genetic Epidemiology

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Ruiz-Narvaez, Edward; Staff;
  • Prerequisites: EPID 701 or EPID 503 or EPID 600 or EPID 601 AND EPID 709 or BIOSTAT 501 or BIOSTAT 521
  • Description: This course familiarizes students with methods and principles of genetic and epigenetic epidemiology. The course integrates concepts in human genetics, population genetics, epidemiology and biostatistics. The course will emphasize applications of existing methods. Topics to be included are population genetics, gene-environment interaction, genetic and epigenetic association studies, and social epigenomics.

EPID722 Medical Product Epidemiology and Global Regulation

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Staff
  • Description: This course addresses the use and effects of medical products -These products are regulated worldwide. These regulatory requirements have stimulated the need for data and varied studies on very large populations to establish the safety of the products and the concomitant conditions that help determine their safety and effectiveness.

EPID743 Applied Linear Regression

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Summer 2016
  • Prerequisites: Intro Epidemiology and Biostatistics and Perm. Instr
  • Description: This course is an introduction to the most powerful analysis technique in statistics: linear regression. This course discusses the applications of linear regression models to medical research and public health data. We will focus on the two major goals of linear models: (1) Explanation: the estimation of associations, and (2) Prediction: the use of models to predict subject outcomes, as with diagnostic tests. Specific topics include graphical exploratory data analysis, assumptions behind simple and multiple linear regression, use of categorical explanatory variables, identification of appropriate transformations of explanatory and/or outcome variables, assessment of predictor/outcome associations through hypothesis testing, identification of confounding and effect modification, assessment of model fit, and model selection techniques.

EPID761 Social Determinants Of Population Health

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Mendes de Leon, Carlos
  • Description: This course will provide an introduction theories, concepts, methods, and findings in recent social epidemiologic research. 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 race/ethnicity, social status, features of the neighborhood social environment, and individual-level psychosocial characteristics.

EPID778 Spatial Statistics for Epidemiological Data

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Berrocal, Veronica
  • Last offered Summer 2016
  • Description: 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
  • Course Goals: 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
  • Competencies: 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

EPID780 APPLIED EPIDEMIOLOGIC ANALYSIS FOR CAUSAL INFERENCE

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Staff
  • Prerequisites: EPID 701 or EPID 503 or EPID 600 or EPID 601 AND EPID 709 or BIOSTAT 501 or BIOSTAT 521
  • Description: This course focuses on regression models of potential outcomes for the estimation of causal parameters in epidemiologic research. Emphasis is on understanding the causal models, generating analysis with software code, and interpreting the resulting estimates.

EPID793 Complex Systems Modeling for Public Health Research

  • Graduate Level
  • Summer term(s)
  • 2 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Summer 2016
  • Prerequisites: None
  • Description: 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.

EPID799 Qualitative Methods for Epidemiology

  • Graduate Level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Summer 2016
  • Prerequisites: none
  • Description: This course provides an overview of qualitative research methods that can complement and enhance epidemiologic studies. It is useful for epidemiologists interested in understanding the social, cultural and behavioral aspects of public health issues within communities. Students will learn how to integrate qualitative methods into epidemiology research and how to select appropriate qualitative methods. Sessions will cover: principles of qualitative research, study design, participant recruitment, data collection methods (interviews, group discussion, and observation), writing and presenting qualitative research and assessing research quality. The course uses participatory learning activities to build core skills. The course is valuable for public health professionals, staff at government and non-government agencies focusing on health and disease, graduate students and researchers. Skills learnt in this course will be valuable for conducting epidemiology research and evaluating qualitative research components in funding proposals, projects and publications.
  • Course Goals: Students will learn how to integrate qualitative methods into epidemiology research and how to select appropriate qualitative methods. Sessions will cover: principles of qualitative research, study design, participant recruitment, data collection methods (interviews, group discussion, and observation), writing and presenting qualitative research and assessing research quality. The course uses participatory learning activities to build core skills.
  • Competencies: Students will learn how to integrate qualitative methods into epidemiology research and how to select appropriate qualitative methods. Sessions will cover: principles of qualitative research, study design, participant recruitment, data collection methods (interviews, group discussion, and observation), writing and presenting qualitative research and assessing research quality. The course uses participatory learning activities to build core skills.

EPID891 Advanced Readings in Epidemiology

  • Graduate Level
  • Fall, Winter, Spring, Summer term(s)
  • 2 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Winter 2015
  • Prerequisites: Perm. Instr.
  • Description: Students will review assigned readings on the epidemiology or natural history of specific infections or chronic diseases or on host or environmental factors associated with disease, or on epidemiological methods and their application. May be elected more than once

EPID970 Pre-candidacy research in Epidemiology

  • Graduate Level
  • Fall, Winter, Spring, Spring-Summer, Summer term(s)
  • 1-8 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Winter 2015
  • Prerequisites: Doctoral Student in Epidemiology Standing
  • Description: Original investigations in the various fields of Epidemiology as part of the student's preparation for their dissertation research and writing.

EPID990 Dissertation Research/Pre-Candidate

  • Graduate Level
  • Fall, Winter, Spring, Summer term(s)
  • 1-8 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Winter 2015
  • Description: For students who have NOT reached candidacy yet.

EPID995 Dissertation Research/Candidate

  • Graduate Level
  • Fall, Winter, Spring, Summer term(s)
  • 8 Credit Hour(s)
  • Instructor(s): Staff
  • Last offered Winter 2015
  • Description: Election for dissertation work by doctoral student who has been admitted to status as a candidate