1. Course Aim The course will cover the foundations of pharmacoepidemiologic research on the health effects of drug-drug interactions (DDI) in populations. We will introduce the main sources of bias in drug-drug interaction studies, cover main study designs and techniques to address bias, introduce the concepts for high-throughput data mining for drug-drug interactions, and walk the audience through several case studies.
2. Requisites Statement This is an intermediate level course. Attendees should have basic knowledge of epidemiology or pharmacoepidemiology. The following papers are recommended for preparation for the course, but are not required:
Hennessy S et al. Pharmacoepidemiologic methods for studying the health effects of drug-drug interactions. Clin Pharmacol Ther 2016;99(1):92-100. Bykov K, Franklin JM, Li H, Gagne JJ. Comparison of self-controlled designs for evaluating outcomes of drug-drug interactions: Simulation study. Epidemiology 2019;30(6):861-866.
3. Course Objectives - To describe the role of pharmacoepidemiology in generating real-world evidence on the population health effects of drug interactions - To describe main sources of bias in drug-drug interaction research - To describe the application of a cohort design to study the population health effects of drug interactions - To introduce self-controlled designs and their application to identify and evaluate clinically relevant drug-drug interactions. - To present new and advanced topics in drug-drug interaction research, including but not limited to causal inference, study design bias, high-throughput screening, or recent publications. - To facilitate discussion of challenges and solutions in drug-drug interaction research
4. Syllabus Outline Introduction, format, and objectives of the course | Presenter: Katsiaryna Bykov, PharmD, ScD (Brigham and Women’s Hospital and Harvard Medical School)
Lecture 1: Introduction to drug-drug interaction research | Presenter: Todd Miano, PharmD, PhD, FCCM (University of Pennsylvania)
This lecture will provide an overview and relative merits of available research designs for studying the health effects of drug-drug interactions. Further, it will describe methodologic issues and biases that are unique to or more prominent in studies of drug-drug interactions compared to those examining individual drugs.
Case-studies | Design-related decisions for cohort studies on drug-drug interactions | Presenter: Antonios Douros, MD, PhD (McGill University)
This interactive talk will focus on the cohort design for the study of drug-drug interactions. Using examples Dr. Douros’s research, the talk will describe the decision-making process with respect to important aspects of the cohort design (e.g., cohort assembly, exposure definition, choice of comparators) and the minimization of potential biases.
Lecture 2 | Self-controlled designs for drug-drug interactions | Presenter: Katsiaryna Bykov, PharmD, ScD (Brigham and Women’s Hospital and Harvard Medical School)
This lecture will provide an overview of self-controlled designs and their role in DDI research. We will discuss two major self-controlled designs: self-controlled case series and case-crossover, along with their pros and cons. The lecture will include empirical examples of how self-controlled designs are used to identify or evaluate drug-drug interactions in pharmacoepidemiology.
Lecture 3 | Advanced topics: Time-related bias in drug-drug interaction studies | Presenter: Katsiaryna Bykov, PharmD, ScD (Brigham and Women’s Hospital and Harvard Medical School) This lecture will provide examples of how time-related biases, including immortal person-time bias, can be introduced in DDI studies. We will further discuss strategies to identify and avoid time-related bias in DDI research.