Session overviews:
Track A: Designing Transparent and Reproducible Real-World Drug Studies (More suitable for beginners/intermediate)
This track introduces core pharmacoepidemiologic study designs and the strengths and limitations of major real-world data sources. With a focus on transparency, reproducibility, and emerging AI-assisted approaches, participants will gain hands-on skills in prompt engineering for protocol development, statistical analysis in SAS and R, and effective data visualization for pharmacoepidemiologic research.
Track B: Emulating a Hypothetical Drug Trial Using Real-World Data (More suitable for advanced)
This advanced track provides an in-depth introduction to advanced target trial emulation methods, covering database integration and optimization, clone–censor–weighting, and sequential trial-based designs. Through practical exercises and real-world examples, participants will engage in an interactive, hands-on learning experience to develop practical skills in applying these advanced methods to real-world research.
Track C: Machine Learning in Real-World Drug Studies: Introduction to Prediction and Natural Language Processing (NLP) models
This track introduces the foundational concepts and practical applications of machine learning (ML)-based prediction modeling, natural language processing (NLP), and large language models (LLMs) in pharmacoepidemiology and real-world data research. Through presentations and hands-on exercises, participants will learn (1) how LLM-based tools (e.g., codex, Claude code) can support day-to-day research workflow, (2) coding and machine learning-driven disease phenotyping strategies, and (3) critically evaluate epidemiologic biases and methodological challenges in ML-enhanced analyses.
Track D: Learning from Real-World Evidence to Inform Policy and Practice
This policy-focused track examines how real-world drug studies inform regulatory processes, reimbursement decisions in Australia and globally, and using real world data for health economic evaluations of drugs. Participants will understand how evidence is translated into policy and funding decisions.
Please indicate which of the Educational Session streams you wish to attend.
