STATISTICAL SOCIETY OF AUSTRALIA QUEENSLAND BRANCH ANNUAL GENERAL MEETING 2025
Location: QUT Gardens Point campus, room GP-S-314/Zoom
Time: 5 PM to 7 PM
Date: Wednesday, 19th March 2025
Please join us in-person/online for the SSA Queensland Annual General Meeting! We will review the past year's accomplishments and elect the 2025 council.
For those who are unable attend in person, a Zoom link will be sent with registration.
Following the AGM, we'll hear from Professor Angus Ng, Senior Biostatistician at the School of Medicine and Dentistry, Griffith University.
Talk Title: Mixtures of generalised linear mixed models for clustering data with structured dependence
Abstract: Identifying (disadvantaged) subgroups is fundamental in solving many real-world problems. Mixture models underpin a variety of statistical methods in cluster and latent class analyses for finding subgroups, outliers, and distinctive features between subgroups. Statistical inference for mixture models assumes that observed data are independent of one another. However, modern study designs often generate data structures with non-negligible dependence among data (e.g., patients treated in a hospital share the same hospital effect in multilevel studies). Thus, the independence assumption becomes invalid. Model-based clustering methods (or mixture models) that ignore the structured dependence (by assuming zero hospital effect) can overlook the significance of such effect and data variability, resulting in misleading findings or failure to identify important risk factors. We present a statistical framework in mixtures of generalised linear mixed models (GLMMs) for clustering data with complex structured dependence. We introduce random-effect modelling techniques that can effectively capture complex intra- and between-subject correlations among observations due to various forms of dependence. An efficient estimation of model parameters is achieved using extended best linear unbiased prediction (BLUP) and approximate residual maximum likelihood (REML) procedures. We consider examples of dependence among multilevel data, recurrent-event data, and network random-graph data.
Biography: Professor Angus Ng is a senior biostatistician at the School of Medicine and Dentistry, Griffith University. He has demonstrated research leadership in the fields of statistical inference and cluster analysis, particularly in mixture models, the EM algorithm, and random-effects modelling. He has taken a leading role in statistics in research projects funded by the Australian Research Council (ARC) and the Australian National Health and Medical Research Council (NHMRC) including Medical Research Future Fund (MRFF). His work has impacted the field of statistics and other multidisciplinary fields through improving evidence-based practice and decision making. Professor Ng has over 180 publications, including a lead-authored research monograph published by Chapman & Hall USA in 2019. He has served on five MRFF Grant Assessment Committees since 2021, the ARC College of Experts between 2022-2025, and as an expert reviewer for the UK Medical Research Council and Wellcome Trust. He is an Associate Editor of Journal of Statistical Computation and Simulation (JSCS) since 2003.
CALL FOR NOMINATIONS FOR MEMBERS OF BRANCH COUNCIL 2025
Nominations are called for the positions in the Queensland Branch Council for 2025 in accordance with Rule 11b of the Branch. Nominations can be made either using the nomination forms below or sending an email to the SSA Queensland Branch President Hien Nguyen (h.nguyen5@latrobe.edu.au) or Secretary Adrian Barnett (a.barnett@qut.edu.au).
Nominations should be received on or before Wednesday 19th March 2025. Such nominations shall be supported by at least two members, and shall be accompanied by the candidate's written statement of willingness to stand. Nomination forms will also be provided at the AGM. Separate nomination forms are available for councillors and executive positions (President/Secretary/Treasurer).
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