SC-09 - Intermediate Pharmacoepidemiology: Approaches to Unmeasured Confounders

1. Course Aim

This course will introduce students to multiple approaches to addressing unmeasured confounders through study design and analysis, and the requirements and assumptions of each.

2. Requisites Statement

This is an intermediate level course for researchers using healthcare data for pharmacoepidemiology. There are no precourse materials to be reviewed.

3. Course Objectives

· Identify common unmeasured covariates in pharmacoepidemiology and the types of bias that can result.
· Describe multiple approaches to addressing unmeasured covariates in pharmacoepidemiology through study design and analysis.
· Understand the assumptions of each approach, ways of evaluating these assumptions, and when they are likely to be violated.

4. Syllabus Outline

Section 1: Use of study design to control unmeasured confounding: During the first set of lectures, Drs Lund, Raman, and Gokhale will introduce three ways in which study design can be used to limit bias due to unmeasured confounding: a) the active comparator new user (ACNU) design, b) self-controlled designs, and c) instrumental variable designs. Faculty will provide foundational knowledge about each approach illustrated through real-world examples.
· Module 1: Active comparator / new user study design with Dr. Jennifer Lund
· Module 2: Self-controlled study designs with Dr. Sudha Raman
· Module 3: Instrumental variable study designs with Dr. Mugdha Gokhale

Section 2: Analytic approaches to unmeasured confounding: During the second set of modules, Drs Lund, Stürmer, and Jonsson Funk will introduce three analytic approaches to reduce bias due to unmeasured confounding: a) proxy measures, b) external control for confounding, and c) quantitative bias analysis. Faculty will provide foundational knowledge about each approach illustrated through real-world examples.
· Module 4: Proxy measures with Dr. Jennifer Lund
· Module 5: External control for confounding with Dr. Til Stürmer
· Module 6: Quantitative bias analysis with Dr. Michele Jonsson Funk

Format: Live, in person lectures followed by Q&A with the course faculty.