Statistical methods for quantifying variation in spatiotemporal areal data. This project aims to develop new statistical methods for extracting insights into spatial and temporal variation in areal data. These tools will extend the Australian Cancer Atlas which provides small area estimates for 20 cancers across Australia. The project is significant because it will allow government and other organisations to reap dividends from investment in collecting spatial information and it will enable mode ....Statistical methods for quantifying variation in spatiotemporal areal data. This project aims to develop new statistical methods for extracting insights into spatial and temporal variation in areal data. These tools will extend the Australian Cancer Atlas which provides small area estimates for 20 cancers across Australia. The project is significant because it will allow government and other organisations to reap dividends from investment in collecting spatial information and it will enable modelled small-area estimates to be released without compromising confidentiality. The expected outcomes include new statistical knowledge and new insights into cancer. The results will benefit the many disciplines, managers and policy makers that make decisions based on geographic data mapped over space and time. Read moreRead less
A Program Of Methodological And Collaborative Research In Biostatistics And Population Health
Funder
National Health and Medical Research Council
Funding Amount
$264,081.00
Summary
Biostatistics is a critical component of health and medical research, especially for studies in population health. However, there is an increasing gap between supply and demand for high-level biostatistical input. This proposal combines novel methodological research into methods for analysing incomplete data, with collaborative research applying new ideas and complex analyses to important health problems. The fellowship will facilitate my development as a future leader in this key area.
Developing And Applying Biologically Plausible Statistical Models For Normal And Non-normal Family Data
Funder
National Health and Medical Research Council
Funding Amount
$339,700.00
Summary
Although molecular and computing advances have enabled more detailed investigations of inherited diseases and the ability to fit realistic statistical models to these data, limitations still exist when analysing family data. Often only basic statistical analyses are performed, due to the lack of understanding of complexities within the data and-or inability of researchers to fit appropriate statistical models. These factors have hampered the search for genes and environmental factors influencing ....Although molecular and computing advances have enabled more detailed investigations of inherited diseases and the ability to fit realistic statistical models to these data, limitations still exist when analysing family data. Often only basic statistical analyses are performed, due to the lack of understanding of complexities within the data and-or inability of researchers to fit appropriate statistical models. These factors have hampered the search for genes and environmental factors influencing common diseases. This project aims to develop novel, biologically realistic statistical models for investigation of common, complex diseases, such as heart disease and cancer, in families. These models will incorporate both measured and unmeasured genetic and environmental factors, and will be applicable to both normally distributed and non-normally distributed traits. Model fitting will use computer-intensive simulation techniques. Application of the models to data from two large pre-existing studies of international renown, the Victorian Family Heart Study and the Australian Prostate Cancer Family Study, will enable a better understanding of the genetic and environmental factors influencing heart disease and cancer. The models will also be applicable to many other studies of diseases which use data from families, and allow more accurate and useful information to be obtained from data. Software will also be made freely available to other researchers. This will ultimately translate into better outcomes from familial genetic research, and eventually, better prevention, detection, and treatment of the diseases.Read moreRead less
Binary regression with additive predictors: new statistical theory with healthcare applications. This project will develop new statistical analysis techniques for predicting whether someone is at risk of adverse health outcomes. The project will then apply the new techniques to a large database on heart attacks, leading to new insights into how patient characteristics and treatments affect the chance of dying from a heart attack.
A likelihood-based approach to combined surveys inference. This project focuses on the development of statistical theory for efficient integration of information across multiple complex sample surveys. It will develop theory and methodology that will answer complex questions about relationships between important social, economic and health related variables that are presently measured in separate surveys.