8 Feb 2023 09:00am to 05:00pm

Summer School 2023: Causal analysis methods: beyond standard regression

Event Location
Ground Floor Conference Rooms 2 & 3
Monash University School of Public Health and Preventive Medicine, 553 St Kilda Road
Melbourne VIC 3004
Margarita Moreno Betancur
Margarita is co-lead of the Clinical Epidemiology and Biostatistics Unit (CEBU) at the MCRI and the University of Melbourne. Since completing her PhD in Biostatistics at Université Paris-Sud in 2014...
Prof John Carlin
John holds appointments with the Murdoch Children’s Research Institute and The University of Melbourne. Since completing a PhD in Statistics at Harvard University he has been engaged as a collaborator...
Ghazaleh Dashti
Ghazaleh completed her PhD in Epidemiology at the University of Melbourne in 2020. Her current research focuses on methods for handling missing data in the context of causal inference methods...
Marnie is a biostatistician at the Murdoch Children's Research Institute. She completed her PhD at The University of Melbourne in 2020, investigating the application of multilevel regression and poststratification for...
Daisy completed her PhD at the University of Auckland in 2019, researching and developing goodness-of-fit methods for phylogenetic models. Her current research focuses on modern approaches to causal inference, with...
Topic: Evaluation and development of approaches for conducting sensitivity analyses within the multiple imputation framework.
Tong completed his PhD at the University of Auckland in 2022. His PhD research focused on the optimal sampling for regression models in two-phase health studies. His current research focuses...
Jiaxin Zhang
Topic : Development and evaluating of causal inference methods for epidemiological studies with missing data
Susie is undertaking a PhD in data-adaptive methods for causal inference using machine learning.

In this era of “data science” it is vitally important to clearly articulate the questions that we ask of data, understand the challenges inherent in answering different types of questions, and ensure that our analysis methods are suitably aligned. An overwhelming number of clinical and public health research studies ask causal (“what if…”) questions, about the effects of treatments, policies, behaviours and other exposures on health outcomes. Answers to these questions are key to guiding decision making in health policy and practice when the goal is to improve patient outcomes and population health. Causal inference requires carefully structured reasoning to guide appropriate statistical analysis.

This workshop provides an overview of key concepts relating to the design of causal analyses and reviews the standard regression method for estimating causal effects, before moving on to the main focus which is on two alternative estimation methods that are less prone to bias: g-computation and inverse probability (or “propensity score”) weighting (IPW). Lectures and tutorials will help ground understanding of these methods, emphasising underlying intuition and assumptions, whilst a hands-on computer practical (in R and Stata) will cover their implementation in practice. All lectures and tutorials include illustrations from real-world observational epidemiological studies. Electronic copies of presentation materials will be made available online.

Prerequisites: The target audience is statisticians and researchers with some statistical background, including some knowledge of regression methods. It is desirable that participants are familiar with the distinction between the three types of research question (description/prediction/causal inference) and the target trial framework for defining causal effects and designing causal analyses (such as covered in the course "Observational studies: Modern concepts & analytic methods(link is external)" delivered by the Clinical Epidemiology and Biostatistics Unit (CEBU) at the Melbourne Children’s campus).

For the computer practical, students must also have a sound working familiarity with Stata or R and have the corresponding software installed on their computer or laptop.