Clinical Epidemiology – HST194 (Intermediate), since 2017
Instructor: Miguel Hernán
How do we learn what we know in clinical medicine? How do we quantify disease burden, identify risk factors for a health condition, and determine whether an intervention is effective for the treatment or prevention of disease? This course introduces epidemiologic methods for the generation, analysis, and interpretation of data in clinical research. The course is organized around the three main tasks of clinical research: description, prediction, and causal inference.
Besides the essentials of descriptive and predictive analytics, the course discusses causal inference based on randomized trials, clinical cohorts, and analyses of electronic health records. Students will learn to formulate well-defined research questions, to discuss the adequacy of research data, to evaluate algorithms for clinical prediction, to critically assess causal inference studies, and to identify and prevent biases in clinical research. Familiarity with regression modeling and intermediate statistics is a pre-requisite. The course emphasizes critical thinking, including daily assignments based on articles published in major clinical journals and the discussion of weekly case studies. A key goal of the course is training students to comprehend, critique, and communicate research findings from the medical literature.
After successful completion of the course, students will be able to:
- Recognize, classify, and formulate well-defined questions in clinical research
- Design data collection for descriptive and predictive studies
- Evaluate algorithms for clinical prediction
- Discuss the use of randomized trials for causal inference
- Discuss the use of observational analyses for causal inference
- Outline critical evaluations of the clinical literature
Spring Semester, Harvard-MIT Division of Health Sciences and Technology
Introduction to Biostatistics and Epidemiology – HST190/191 (Introductory), 2005-2015
Instructors: Rebecca Betensky, Miguel Hernán
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