Contemporary Epidemiology

Register here

Start date: 16 November 2026

Duration: 3 days (16–18 November 2026)

Study mode: In person

Location: University of Melbourne, Parkville

Fees: Early Bird $1,400 (closes 4 October 2026) / Standard $1,650 (inc. GST)

What you will learn
This short course introduces the concept of causation in public health research and the methods used to draw causal inferences from observational data in epidemiology. Building from the major sources of systematic error in observational research, participants will explore contemporary epidemiological concepts and methods, with a particular emphasis on understanding the fundamental challenges of observational epidemiology and the approaches available to address them using a potential outcomes perspective.

Rather than focusing on mastery of a single technique, the course introduces the range of concepts and methods that make up the contemporary epidemiological toolbox. Participants will leave with a stronger understanding of causal thinking, greater confidence engaging with contemporary epidemiological literature, and a framework for critically evaluating research and evidence used to inform public health policy and practice.

Who is this course designed for?
The course is designed for two broad audiences. First, PhD students, advanced Master of Public Health students and early-career researchers who wish to deepen their understanding of contemporary epidemiological methods and causal inference. Second, more experienced investigators, clinicians or policy practitioners seeking an efficient update on the new thinking and methods increasingly used in high-quality epidemiological research.

Whether you are building your epidemiology skills or refreshing existing knowledge, the course provides a practical framework for engaging with contemporary epidemiological literature and evidence.

Course prerequisites
Participants should have a working knowledge of epidemiological study design, biostatistics and regression methods, including familiarity with confounding, information bias and selection bias.

Completion of a Master of Public Health, postgraduate epidemiology training, or equivalent practical experience will generally provide the necessary background.

If you would like to discuss whether the course is right for you, please contact population-interventions@unimelb.edu.au

Course dates
Dates: 16–18 November 2026
Location: University of Melbourne, Parkville
Format: In person

Fees: Early Bird $1,400 (closes 4 October 2026) / Standard $1,650 (registrations received less than six weeks before course commencement) Morning tea, lunch and afternoon tea will be provided each day.

Discounts may apply (limits per course) for PhD students, University of Melbourne staff and groups. Please contact population-interventions@unimelb.edu.au for further information.

Course outline

Day 1: Causation, Confounding and Information Bias

  • Causal concepts in epidemiology
  • Causal effects and counterfactuals
  • Directed acyclic graphs (DAGs)
  • Confounding
  • Information bias

Day 2: Selection Bias, QBA and Causal Methods

  • Selection bias
  • Introduction to quantitative bias analysis
  • Quantitative bias analysis exercises
  • Confounding adjustment

Day 3: Causal Methods and Implications

  • Interaction and effect modification
  • Causal mediation analysis
  • Target trials
  • Transportability of epidemiological measures across time, place and person
  • Policy implications – what are we trying to maximise anyway? Longevity? Morbidity compression? The economy?

By the end of the course, participants will be able to:

  • Have a thorough understanding of the potential outcomes/counterfactual approach to defining causal effects
  • Be able to draw Directed Acyclic Graphs (DAGs) to inform analysis plans
  • Understand the key sources of systematic error (confounding, information bias, selection bias) in analyses of observational data, from a counterfactual and DAG perspective
  • Conduct quantitative bias analysis to correct for systematic error, providing tools to use in research and a deeper understanding of systematic error
  • Be familiar with a range of confounder adjustment methods which can be used to estimate causal effects and understand assumptions underlying them
  • Appreciate the uses and assumptions underlying methods to explore mechanisms in cause-effect relationships (interaction & effect modification, causal mediation analysis)
  • Understand the principles of target trial emulation and how to implement them in epidemiological research
  • Have the necessary conceptual foundations to further explore the causal inference literature and undertake critical appraisal
  • A critical perspective on how epidemiology can be used to assist health and public policy

Course leaders
Professor Tony Blakely is Head of the Population Interventions Unit and the Scalable Health Intervention Evaluation (SHINE) program within the Melbourne School of Population and Global Health. An internationally recognised epidemiologist, Tony's research focuses on quantifying the health gains, equity impacts and economic consequences of public health interventions.

Associate Professor Zoe Aitken is a Principal Research Fellow in Social Epidemiology within the Melbourne School of Population and Global Health and an ARC Industry Fellow. Her research focuses on understanding and reducing health inequalities, using contemporary epidemiological methods to produce policy-relevant evidence that can be translated into action.

Guest lectures
Dr Frances Albers is a recent PhD graduate in cancer epidemiology, with interests in counterfactual analysis, competing mortality and mediation analyses.

Registration procedure
Registrations close four days prior to course commencement.

For participants registering close to the course date, please be aware that we may not be able to accommodate all dietary requests or other special requirements.

Register here

For further enquiries: Population Interventions Unit
E: population-interventions@unimelb.edu.au