Teaching
Epidemiologic Methods I - EPI201
Instructor: Miguel Hernán
EPI201 introduces epidemiologic methods for description and causal inference. The course discusses conceptual and practical issues encountered in the design, conduct, and analysis of epidemiologic studies. These fundamental concepts are studied in the simplified context of closed populations with time-fixed exposures, nonparametric methods for data analysis, and absence of sampling variability. The final exam requires the application of the learned skills to a real problem in epidemiology. EPI201 is the first course in the series of methods courses designed for students majoring in Epidemiology or Biostatistics, and for those interested in a detailed introduction to epidemiologic methods. Students who take EPI201 are expected to take EPI202 (Methods II).
Fall Semester, Harvard School of Public Health
EPI201 web site
Models for Causal Inference - EPI289
Instructor: Miguel Hernán
EPI289 describes models for causal inference, their assumptions, and their practical application to epidemiologic data. The course introduces propensity score methods, the parametric g-formula, inverse probability weighting of marginal structural models, g-estimation of nested structural models, and instrumental variable methods. The course also introduces models for causal inference in the presence of time-varying exposures, which will be extensively studied in EPI207. EPI289 is designed to be taken after EPI201/EPI202. The epidemiologic concepts and methods studied in EPI201/202 will be reformulated within a modeling framework in EPI289. Familiarity with the SAS language is strongly recommended.
Spring Semester, Harvard School of Public Health
EPI289 web site
Advanced Epidemiologic Methods - EPI207
Instructors: Eric Tchetgen Tchetgen and Miguel Hernán
This course provides an in depth investigation of statistical methods for drawing causal inferences from observational studies. Informal epidemiologic concepts such as confounding, comparability, overall effects, direct effects, intermediate variables, and selection bias are formally defined within the context of a counterfactual causal model. Methods for the analysis of the causal effects of time-varying exposures in the presence of time dependent covariates that are simultaneously confounders and intermediate variables are emphasized. These methods include g-estimation of structural nested models, inverse probability weighted estimators of marginal structural models, and g-computation algorithm estimators. As a practicum, students reanalyze data sets using the above methods.
Fall semester, Harvard School of Public Health
EPI207 web site
Introduction to Biostatistics and Epidemiology - HST190/191
Instructors: Rebecca Betensky, Miguel Hernán, Yves Chretien, Christina Mills
This course presents
the fundamentals of biostatistics and epidemiology with the aim of training
students to comprehend, critique and communicate findings from the biomedical
literature. In the first part of this course, students will learn how to assess
the importance of chance in the interpretation of experimental data. Major
topics covered include probability theory, normal sampling, chi-squared and
t-tests, analysis of variance, linear regression and survival analysis, as well
has how to perform elementary calculations using the statistical package STATA.
In the second part of this course, students will learn how to identify and prevent
bias in observational studies. Students will learn about causal inference,
types of bias (confounding, selection and measurement bias), and key study
designs (randomized trials, cohort studies and case-control studies).
Winter IAP, Harvard-MIT Division of Health Sciences and Technology
HST190 web site
