WORKSHOP 1 - Full Day Wednesday 18 Oct - 9:00am to 5:00pm

Title: Causal analysis methods: beyond standard regression
Room: Ballroom 1

Description: In this era of “data science” it is vitally important to articulate clearly the questions that we ask of data, understand the challenges inherent in answering different types of question, and ensure that our analysis methods are suitably aligned. Many 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 will combine lectures and tutorials to provide an understanding of key concepts relating to the design of causal analyses, and analysis methods beyond standard regression, specifically g-computation and inverse probability (or "propensity score") weighting (IPW) for estimating causal effects. Emphasis will be on the underlying intuition and assumptions, whilst a hands-on computer practical (in R and Stata) will cover implementing estimation methods in practice. All lectures and tutorials will include illustrations from observational epidemiological studies. Electronic copies of presentation materials will be made available to course attendees.
Target Audience: The target audience is epidemiologists 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)" to be delivered on 9-13 October 2023 online via Zoom 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 laptops.

Laptops: For the computer practical, students must also have a sound working familiarity with Stata or R and have the corresponding software installed on their laptops.
For Stata, no additional packages need to be installed beforehand.
For R, the following packages need to be loaded: “readstata13”, “tableone”, “boot”, “ggplot2”, “gtools”, “survey”, “margins” and "quantreg". These packages are all available on CRAN and can be installed using the command:
install.packages(c("readstata13", "tableone", "boot", "ggplot2", "gtools", "survey", "margins","quantreg"))

Presenters: 
A/Prof Margarita Moreno-Betancur, Principal Research Fellow, CEBU Co-Director, MCRI and University of Melbourne. 
Professor John Carlin, Senior Principal Research Fellow, MCRI and University of Melbourne
Dr Ghazaleh Dashti, Biostatistician, MCRI and University of Melbourne
Dr Marnie Downes, Biostatistician, MCRI and University of Melbourne

Dr Daisy Shepherd, Postdoctoral Research Fellow, MCRI and University of Melbourne
Dr Rushani Wijesuriya, Biostatistician, MCRI and University of Melbourne

Dr Tong Chen, Biostatistician, MCRI and University of Melbourne