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
Bayesian statistical methods for enhancing evidence-based practice in Australia's hospitals. This project addresses Australia's national research priority of Promoting and Maintaining Good Health with the goal of Preventative Healthcare. Through enhanced capability in combining information from diverse sources for improved evidence-based decisions and true sharing of university and medical expertise, the project will enhance Australia's medical research and practice, align professional and comm ....Bayesian statistical methods for enhancing evidence-based practice in Australia's hospitals. This project addresses Australia's national research priority of Promoting and Maintaining Good Health with the goal of Preventative Healthcare. Through enhanced capability in combining information from diverse sources for improved evidence-based decisions and true sharing of university and medical expertise, the project will enhance Australia's medical research and practice, align professional and community expectations, utilise local medical information, and address national demands for quality science underpinning health decisions.
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Making the Most of Database Information in Patient-Based Decision-Making - A Bayesian Approach. This project addresses Australia's national research priority of Promoting and Maintaining Good Health and will lead to immediate improvement in health outcomes through optimising patient outcomes in Australian hospitals; cross-disciplinary and cross-hospital communication. Through enhanced capability in combining information from diverse sources the project will enhance Australia's medical research a ....Making the Most of Database Information in Patient-Based Decision-Making - A Bayesian Approach. This project addresses Australia's national research priority of Promoting and Maintaining Good Health and will lead to immediate improvement in health outcomes through optimising patient outcomes in Australian hospitals; cross-disciplinary and cross-hospital communication. Through enhanced capability in combining information from diverse sources the project will enhance Australia's medical research and practice, align professional and community expectations, utilise increased amounts of local medical information, and address national demands for quality science underpinning health decisions.Read moreRead less
Innovative Bayesian statistics for enhanced decision making for pharmaceutical drug development. This project addresses Australia's national research priority of Promoting and Maintaining Good Health, and will lead to improvements in health outcomes by patients accessing new drugs sooner, enhancing research, and reducing the cost of health care. Decision making in early stages of drug development will be improved by using novel statistical methods, which could also be applied in a variety of sit ....Innovative Bayesian statistics for enhanced decision making for pharmaceutical drug development. This project addresses Australia's national research priority of Promoting and Maintaining Good Health, and will lead to improvements in health outcomes by patients accessing new drugs sooner, enhancing research, and reducing the cost of health care. Decision making in early stages of drug development will be improved by using novel statistical methods, which could also be applied in a variety of situations involving decision making procedures. This will be achieved through cross-disciplinary and university-industry collaboration, utilising Australian and international expertise. National demands for quality science and trained researchers underpinning health decisions will be contributed to.Read moreRead less
Analysing Genetic And Environmental Risk Factors And Their Interactions For Common Cancers And Cardiovascular Disease
Funder
National Health and Medical Research Council
Funding Amount
$129,937.00
Summary
The statistical models for analysing cancer and cardiovascular risk factor family data are important for understanding the genetic and environmental aetiology of these diseases, but complicated by the different levels of correlations between relatives in a family. The conventional assumption of independence in observations is invalid in these situations. We intend to develop, test, implement and distribute a comprehensive suite of new statistical methods designed specifically to assistant molecu ....The statistical models for analysing cancer and cardiovascular risk factor family data are important for understanding the genetic and environmental aetiology of these diseases, but complicated by the different levels of correlations between relatives in a family. The conventional assumption of independence in observations is invalid in these situations. We intend to develop, test, implement and distribute a comprehensive suite of new statistical methods designed specifically to assistant molecular geneticists and genetic epidemiologists undertake informative and meaningful analyses of the measured and latent genetic and environmental risk factors and their possible interactions. The two associate investigators, Prof John Hopper and Prof Stephen Harrap, will bring their respective genetic epidemiological and biometric statistical expertise and their prestigious family data resources to this project. With the suite of flexible statistical models and analyses, we will further our knowledge about genetic and environmental risk factors and their interactions of common cancers and major gene effects for cardiovascular phenotypes. Simulation studies will help us understand some phenomena accounted in the research but cannot be replicated in reality and assess the efficiency of the statistical methods and credibility of our analysis results independently. Statistical programs developed in this project can also be used in other genetic and epidemiological studies (e.g. diabetes, epilepsy) where such high-level statistical tools are not yet available.Read moreRead less
New nonparametric statistical methods for imperfectly observed data. Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutriti ....New nonparametric statistical methods for imperfectly observed data. Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutrition, this project aims to develop novel and highly effective statistical methodology for solving contemporary problems involving new types of imperfectly observed data. The expected outcomes will solve frontier problems, where information can only be accessed through sophisticated computer intensive methods.Read moreRead less
Statistical challenges involving indirect data. This project aims to develop statistical methodology for solving contemporary problems involving indirectly observed data whose complexity is exacerbated by factors such as incompleteness or episodic availability. Modern statistics find it difficult to analyse complex data which contain important information only in an indirect way, such as data measured with noise or aggregated data. This project considers both finite dimensional data and function ....Statistical challenges involving indirect data. This project aims to develop statistical methodology for solving contemporary problems involving indirectly observed data whose complexity is exacerbated by factors such as incompleteness or episodic availability. Modern statistics find it difficult to analyse complex data which contain important information only in an indirect way, such as data measured with noise or aggregated data. This project considers both finite dimensional data and functional data. The expected methodology will be able to solve frontier problems, where only sophisticated methods can access information. This is expected to benefit brain studies, economics, infectious disease, nutrition and public health.Read moreRead less
Doing Bayesian Statistics Better: an Inter-Disciplinary Perspective for Improving Models, Priors, Design and Applications. Through improving methods for data analysis and design, this project increases the capability of individuals, communities and governments to make correct decisions based on data, leading to immeasurable human, social and financial benefits. It will also directly enhance Australia's international research reputation, promote inter-disciplinary links, promote research by wome ....Doing Bayesian Statistics Better: an Inter-Disciplinary Perspective for Improving Models, Priors, Design and Applications. Through improving methods for data analysis and design, this project increases the capability of individuals, communities and governments to make correct decisions based on data, leading to immeasurable human, social and financial benefits. It will also directly enhance Australia's international research reputation, promote inter-disciplinary links, promote research by women in a non-traditional area, keep intellectual property within Australia, train quality undergraduates and postgraduates, and contribute to public good through its focus on applications in key national priorities: health, environment and genetics. Read moreRead less
Nonparametric data analysis in statistical science. Changes in technology have enabled new types of data to be collected, often more complex and in much larger quantities than ever before, and altered fundamentally the types of questions that need to be asked of those data. The research program will develop new statistical methods for analysing new types of data, for example functional data and data with many dimensions, and will also introduce greatly improved solutions to problems that involve ....Nonparametric data analysis in statistical science. Changes in technology have enabled new types of data to be collected, often more complex and in much larger quantities than ever before, and altered fundamentally the types of questions that need to be asked of those data. The research program will develop new statistical methods for analysing new types of data, for example functional data and data with many dimensions, and will also introduce greatly improved solutions to problems that involve more conventional data types. These techniques will have critical applications to diverse fields. The program will contribute substantially to capacity building in a strategically important area, statistical science, of great value to Australia but where chronic skills shortages exist.Read moreRead less
Statistical Modelling in the Era of Data Science: Theory and Practice. This project aims to develop innovative statistical methodology that is interpretable, theoretically justified, and scalable to today's growing complex data. With the influx of data being collected in both the public and private sectors, making sense of this data is a fundamental task. Through a rigorous modelling framework, this project intends to facilitate the discovery of knowledge by developing powerful new tools to extr ....Statistical Modelling in the Era of Data Science: Theory and Practice. This project aims to develop innovative statistical methodology that is interpretable, theoretically justified, and scalable to today's growing complex data. With the influx of data being collected in both the public and private sectors, making sense of this data is a fundamental task. Through a rigorous modelling framework, this project intends to facilitate the discovery of knowledge by developing powerful new tools to extract insight from these complex datasets. The outcomes of this project will benefit society by providing techniques to enable research advances and inform decision-making for a broad base of disciplines, including applications to network security, energy forecasting, environmental monitoring, and public health. Read moreRead less