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STATISTICAL SOCIETY OF AUSTRALIA QUEENSLAND BRANCH ANNUAL GENERAL MEETING 2021
AGM at 4pm, followed by talk at 5pm
Title: Modelling small area summary estimates from online disease atlas: Bayesian meta-analysis approach
Speaker: Farzana Jahan
Abstract: Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. A Bayesian hierarchical meta-analysis model is developed to model the area level estimates of disease incidence. An illustration of univariate and multivariate Bayesian hierarchical meta-analysis is made on the estimates of cancer incidence from Australian Cancer Atlas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalised to other online disease maps and atlases.
Bio: I am a Postdoctoral Research Fellow at QUT Centre for Data Science, Queensland University of Technology (QUT), Brisbane, Australia. I submitted my PhD thesis for examination this January. I love both Teaching and Research. I like to do research on broader field of Statistics and Data Science and would love to learn new things and work on newer applications. My research interests include Bayesian modelling for aggregate data, spatial modelling of summary measures, Bayesian hierarchical modelling and Bayesian meta-analysis. ORCID page.
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