Educational sessions
TRACK A: Database Pharmacoepidemiology (Introductory/Intermediate) – In this course, we will introduce participants to the types of healthcare databases used in pharmacoepidemiology research; discuss how to design and evaluate studies based on these datasets; and explore the practical necessities of drafting study protocols and implementing statistical coding.
TRACK B: Non-Database Pharmacoepidemiology (Introductory/Intermediate) – In this course, we will introduce participants to various study designs in pharmacoepidemiology, and how to write a protocol, perform statistical analysis, and critically appraise a non-database pharmacoepidemiologic study.
TRACK C: Artificial intelligence tools to support pharmacoepidemiology research
In this course, we will introduce participants to recent developments in artificial intelligence and how these tools can be implemented in your pharmacoepidemiology research workflow.
The instructors will introduce participants to fundamental concepts related to language and prediction models. The sessions will demonstrate how specific artificial intelligence tools can be used in practice to standardize and streamline common tasks that pharmacoepidemiologists perform throughout a project lifecycle including data collection (e.g., natural language processing of unstructured clinical data), predictive modelling and publishing medication-related studies in digital health or artificial intelligence journals.
Participant prerequisites:
- Basic knowledge of statistics
- Experience using either R or Python software
- Please bring your own laptop with R installed for practical session
TRACK D: Advanced Pharmacoepidemiology – This course will introduce participants to advanced topics in pharmacoepidemiology, with a focus on the pitfalls and nuances of advanced methods in comparative effectiveness research.
