Dr Gary Chan - Adjusting for time-varying confounders using IPTW for causal inference
Speaker
Dr Gary Chan, Senior Research Fellow, National Centre for Youth Substance Use Research, UQ
Abstract
Randomized controlled trial is the gold standard for causal inference yet it is not always feasible. Multi-wave longitudinal data is increasingly used for causal inference. Standard methods such as regression yield biased results in the presence of time-varying confounders. In this seminar, I will demonstrate using Inverse probability treatment weight (IPTW) and marginal structural model (MSM) to adjust for time-varying confounders. Examples in this seminar will be based on research in psychology, public health and epidemiology, and the analyses will be conducted in R and StatsNotebook.
Bio
Dr. Gary Chan is a statistician and epidemiologist at the National Centre for Youth Substance Use Research, University of Queensland. His research focuses on statistical software development, causal inferences, and the epidemiology of substance use among young people. He has served as a consultant at the United Nations Office on Drugs and Crime to evaluate the data collection methodology on global substance use data, and as a biostatistical consultant for the West Moreton Health Service.
About Research-to-Impact Talks
SPPS Research-to-Impact talks are regular informal presentations of high-quality scholarly work with broad appeal and research impact.
The wider School community is invited to attend, including academic and professional staff, special guests, visitors, as well as HDR, postgraduate, masters and honours students.