Conference starts in -973 days
Educational sessions – Friday 30 October 2026
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 and reproducibility, participants will gain hands-on skills in protocol writing, study registration, reporting standards, and practical statistical programming in SAS and R.
Track B: Emulating a Hypothetical Drug Trial Using Real-World Data
(More suitable for advanced)
This advanced track focuses on target trial emulation approaches, including clone–censor–weighting and sequential trial-based design, prevalent new-user design, and nested case-control design. Participants will also explore quantitative bias analysis frameworks to strengthen causal inference.
Track C: Machine Learning in Real-World Drug Studies: Introduction to Prediction and Natural Language Processing (NLP) models
This track introduces machine learning-based prediction modelling and NLP applications in real-world drug studies. Participants will gain practical exposure to basic coding workflows, machine learning–driven disease phenotyping strategies, while learning to critically evaluate epidemiologic biases in NLP-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.