Analytical Clinical Epi
EPI 236
Subject & Catalog Number
Course Information
Description
Course Description: This course will cover classical and modern analytical methods for clinical epidemiology, including regression-based methods to improve the causal interpretation of findings from population-based studies (confounding adjustment, propensity score methods), and to develop clinical prediction models. While primary emphasis will be given on applications and extension of logistic regression, the course will also introduce the application of machine learning (e.g. random forests, gradient boosting, neural networks) in clinical epidemiology. Lectures will discuss these techniques and methodologies always maintaining an applied perspective, and weekly lab sessions run by Teaching Fellows will demonstrate how to perform these analyses using Stata and R. Students can use any computer software packages to complete course assignments.
Course Objectives: Upon successful completion of this course, students should be able to: • Identify and apply appropriate analytic methods to examine the effect of a risk factor on an outcome. • Detect and control for confounding. • Detect and describe the presence of effect modification and clinical interactions. • Develop and validate a clinical prediction model. • Understand the general concepts of machine learning and specific challenges of its implementation in clinical epidemiology • Implement analyses using Stata or R
Pre-Requisites Introductory Courses in Epidemiology (EPI208, EPI201, EPI500) and in Biostatistics (BST206, BST201), or permission of the instructor
Class Notes
Restricted to:
Summer only MPH-CLE
Summer only MPH-QM
Summer only SM1-EPI
Academic year MPH-CLE
Academic year SM1-EPI
All other students can request instructor permission.
THIS CLASS HAS PRIORITY ENROLLMENT
Priority Wave Groups
Wave 1 | PCE Students
Wave 2 | Approved Petitioners
Wave 3 | Approved Petitioners
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Priority Wave Timing
Wave 1 | 5/14/2026 11:00 AM - 5/24/2026 11:59 PM
Wave 2 | 5/25/2026 12:00 AM - 5/27/2026 11:59 PM
Wave 3 | 5/28/2026 12:00 AM - Enrollment Deadline (varies by session)
Any student who does not meet the Wave 1 or Wave 2 criteria can add themselves to the waitlist (if enrollment requirements are met) at any time during the enrollment period. At the beginning of each priority wave, students on the waitlist who meet the Wave’s criteria will be automatically enrolled into any remaining seats in the course (pending no time conflicts)
Not Available for Cross Registration
