Description: Comparative effectiveness and safety studies rely on estimating causal effects of medications (or vaccines or medical devices) on outcomes using observational data, even if the objective is not explicitly stated as such. Over the past four decades, a formalized language and discipline of causal inference has been developed that can be useful in designing, implementing, and communicating observational research, including pharmacoepidemiology studies. With a mix of didactic teaching and hands-on activities, this course will introduce attendees to the concept and terminology of causal inference and equip them with tools to apply causal thinking to questions about medication safety and effectiveness.
Objectives: Objective 1: Develop fluency in the terminology, notation, and basic concepts of causal inference Objective 2: Become proficient in using directed acyclic graphs (DAGs) to represent and communicate structural biases in observational studies Objective 3: Introduce the target trial framework and apply it to the design and implementation of a comparative effectiveness and safety study
Outline: Presentation 1: The first session will introduce participants to basic concepts and terminology of causal inference (e.g., association versus causation, counterfactual theory), and the assumptions of causal inference, including exchangeability, positivity, and consistency, in the context of observational research.
Presentation 2: The second session will introduce directed acyclic graphs (DAGs) as a way to depict structural biases (confounding, selection bias and information bias) in observational data. Attendees will engage in a hands-on activity to practice discussing and drawing DAGs to represent different real-world pharmacoepidemiology study questions.
Presentation 3: The third session will describe the target trial framework as an approach for designing and analyzing pharmacoepidemiology studies to emulate a hypothetical randomized controlled trial. In an interactive exercise, attendees will translate elements of such a hypothetical target trial into parameters for an observational study that could be implemented using real-world healthcare databases.