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.
Description: This course serves as a basic introduction to math modeling in epidemiology, with examples drawn broadly from infectious disease, chronic disease, and social epidemiology. The goal of this course is to give students basic familiarity with a wide range of topics and methods in mathematical modeling for epidemiology.
Prerequisites: BIOSTAT 560 or permission from the instructor
Description: This course will introduce 1) the concepts of multistage carcinogenesis and the analysis of cancer epidemiology using mathematical models of carcinogenesis; 2) the analysis of cancer prevention strategies using Markov cancer natural history models. Students will learn how to develop and fit multistage and cancer natural history models in R.
Prerequisites: Enrolled in Epidemiology MS programs
Description: This capstone research project course is designed for Epidemiology MS students (30-credit or 48-credit CESM programs). Working with their mentor, students are expected to develop an original research project to address public health problems using epidemiologic methods.
Students will have the opportunity to apply what they learned in their coursework to important public health questions. Students will work with a faculty mentor to conduct a literature review, develop a research project, develop and implement an analysis plan, write up the results and discuss the implications of the findings, and present their work in the annual Epidemiology Poster Day.
Students are expected to begin their capstone project in their first term and complete it in the second term of their final year (or only, for one-year programs) of training (three credits per term, for a total of six credits). The Epidemiology Master’s committee will help students find an appropriate mentor. Details regarding the structure of capstone writing products and evaluation guidelines will be provided in the MS Student Handbook.
Learning Objectives: The learning objectives of and skills employed in this course are determined by the specific research project. The list below (which is not exhaustive) provides examples of learning objectives for this course:
1. Assess knowledge gaps in the scientific literature;
2. Develop a scientific research question designed to address a gap in the scientific literature
3. Identify appropriate data sources to address a research question;
4. Better understand the role of data in understanding public health problems;
5. Create a data collection instrument and/or collect data;
6. Analyze data (quantitative or mixed data – including both quantitative and qualitative) to test research hypotheses relevant to public health in a manner that reflects principles of epidemiology (e.g., study design, measurement, confounding, etc);
7. Generate appropriate data visualizations and/or presentations;
8. Communicate the significance, approach, and implications of epidemiological research in a written format appropriate for the target audience;
9. Complete research ethics training through the Program for the Education and Evaluation of Responsible Research and Scholarship (PEERRS). Two modules are required: Human Subjects Research Protections and Responsible Conduct of Research and Scholarship (RCRS).
Advisory Prerequisites: 1) Experience with modeling, such as EPID 793, or good quantitative background including statistics and differential equations. 2) Experience with basic programming in R software, including indexing, functions, if statements, and for-loops.
Description: 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 of these concepts through hands-on model implementation.
Advisory Prerequisites: A previous/concurrent course in intro epidemiology or biostats is strongly recommended (e.g. EPID 701/709). R resources will be available on Canvas before the beginning of the course but prior introductory experience with R is strongly advised.
Undergraduates are allowed to enroll in this course.
Description: This course will provide participants with practical experience in building and interpreting regression models for diverse epidemiological study designs and research questions. We will cover general linear models, including linear, logistic, Poisson, and log-binomial, considering potential confounding and effect measure modification. We will work with real data sets from a variety of application areas.
Learning Objectives: 1. Apply epidemiologic theory and methods to data analysis
2. Select appropriate biostatistical tools for different epidemiologic study designs
3. Employ R programming for epidemiologic data analysis
4. Critically interpret results from epidemiologic studies
