Teaching
Introduction to Epidemiology (Methods I) - EPI201
Instructor: Miguel Hernán
EPI201 introduces the methodological principles of epidemiologic research for
both description and causal inference. The course discusses conceptual and
practical issues encountered in the design 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 those interested in a detailed introduction
to the design and conduct of epidemiologic studies. 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
This course 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, and for infectious disease epidemiology, which will be extensively studied in EPI207 and EPI260/EPI501, respectively. EPI289 is designed to be taken after EPI201/EPI202 and concurrently with EPI204. The epidemiologic concepts and methods studied in EPI201/202 will be reformulated within a modeling framework in EPI289, and the statistical models described in EPI204 will be used throughout EPI289. Some familiarity with the SAS language is recommended.
Spring Semester, Harvard School of Public Health
EPI289 web site
Advanced Epidemiologic Methods - EPI207
Instructors: James Robins 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