Intermediate Pharmacoepidemiology: Approaches to Unmeasured Confounder

‎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
Introductions: Course faculty lead will provide an overview of the course objectives and introduce the ‎presenting Faculty.
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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
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Section 2: Analytic approaches to unmeasured confounding: During the second set of modules, Drs ‎Lund, Stürmer, and Young 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. Jessica Young
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Section 3: Choosing among approaches: A case study
Dr Michele Jonsson Funk will present a case study in which we pose a hypothetical research question ‎and model thinking about the likely unmeasured covariates, considering how each of the approaches ‎might be used in that setting, and weighing the pros/cons of each. This case-study will provide ‎integration across the approaches and give students an opportunity to consider the selection of an ‎appropriate strategy for addressing important unmeasured covariates in light of real-world constraints.
Format: Live, in person lectures followed immediately by a Q&A with the course faculty.‎