Discovery Early Career Researcher Award - Grant ID: DE190101326
Funder
Australian Research Council
Funding Amount
$391,546.00
Summary
Statistical methods for modelling the pathways between cause and effect. This project aims to develop new biostatistical methods for addressing complex analytic questions that arise in studies of the causes of health, social, educational and other outcomes in the course of human life. These questions concern the pathways that explain how intermediate factors contribute to a statistical relationship between a probable cause of a later outcome. Mathematical and statistical innovation is needed to ....Statistical methods for modelling the pathways between cause and effect. This project aims to develop new biostatistical methods for addressing complex analytic questions that arise in studies of the causes of health, social, educational and other outcomes in the course of human life. These questions concern the pathways that explain how intermediate factors contribute to a statistical relationship between a probable cause of a later outcome. Mathematical and statistical innovation is needed to address them. The expected outcomes include a suite of novel methods designed to evaluate the impact of intervening to modify causal pathways, while also accommodating common complexities of data such as incompleteness. This project should provide major benefits to studies in public health, social sciences and economics.Read moreRead less
Dimension reduction and model selection for statistically challenging data. This project aims to develop a deep theoretical understanding of the relationship between various dimension reduction and model selection methods used in statistical model building, and then use this understanding to develop new, improved methods of model building for statistically challenging data. The research will impact on both the theory and practice of statistics, and on substantive fields which collect and analyse ....Dimension reduction and model selection for statistically challenging data. This project aims to develop a deep theoretical understanding of the relationship between various dimension reduction and model selection methods used in statistical model building, and then use this understanding to develop new, improved methods of model building for statistically challenging data. The research will impact on both the theory and practice of statistics, and on substantive fields which collect and analyse these kinds of data. This will provide a significant improvement in the statistical model building in areas such as epidemiology, chemical and ecological sciences. The project is timely because of the increasing collection of large-dimensional, complex, correlated data sets in these and many other fields.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
Developing mathematical models and statistical methods to understand the dynamics of infectious diseases: stochasticity, structure and inference. Infectious diseases remain a major contributor to mortality and illness worldwide. The potential for future severe pandemics also continues to present a substantial threat to our health and well-being. Mathematics and statistics are increasingly becoming part of the arsenal used by governments to combat the invasion and spread of infectious diseases. I ....Developing mathematical models and statistical methods to understand the dynamics of infectious diseases: stochasticity, structure and inference. Infectious diseases remain a major contributor to mortality and illness worldwide. The potential for future severe pandemics also continues to present a substantial threat to our health and well-being. Mathematics and statistics are increasingly becoming part of the arsenal used by governments to combat the invasion and spread of infectious diseases. In such work, three themes have emerged as having the potential to revolutionise the modelling of infectious diseases: stochasticity, structure (both age and spatial), and inference. This project will develop state-of-the-art techniques, at the interface of these themes, of critical importance to understanding the dynamics of infectious diseases.Read moreRead less
Attrition in longitudinal studies: advancing and evaluating statistical methods. Longitudinal studies are a vital tool for monitoring the health and well-being of Australians. They are uniquely placed to examine changes in diseases over time and prospectively collect data on exposure and disease onset. There have been many successful longitudinal studies in Australia that have lead to significant breakthroughs in evidence-based health (e.g. the Nambour Skin Cancer Prevention Trial). Unfortunatel ....Attrition in longitudinal studies: advancing and evaluating statistical methods. Longitudinal studies are a vital tool for monitoring the health and well-being of Australians. They are uniquely placed to examine changes in diseases over time and prospectively collect data on exposure and disease onset. There have been many successful longitudinal studies in Australia that have lead to significant breakthroughs in evidence-based health (e.g. the Nambour Skin Cancer Prevention Trial). Unfortunately all longitudinal studies suffer from attrition, or loss of participants, which leads to questions concerning their validity and generalisability. This project will investigate the causes of attrition, and the effect attrition has on longitudinal studies, in order to improve their design and analysis.Read moreRead less
Prediction, inference and their application to modelling correlated data. This project aims to create new, improved methods for prediction and making inference about predictions for a variety of correlated data types through inventing sophisticated and novel resampling schemes such as the generalised fast bootstrap and repeated partial permutation. The research will impact on both the theory and practice of statistics and on substantive fields which use mixed or compositional models to analyse d ....Prediction, inference and their application to modelling correlated data. This project aims to create new, improved methods for prediction and making inference about predictions for a variety of correlated data types through inventing sophisticated and novel resampling schemes such as the generalised fast bootstrap and repeated partial permutation. The research will impact on both the theory and practice of statistics and on substantive fields which use mixed or compositional models to analyse dependent data. This will be a significant improvement in the assessment and stability of statistical models in areas such as social, ecological and geological sciences.Read moreRead less
Modeling Healthcare Systems. An efficient healthcare system is essential for the well-being of any society. The aim of the project is to develop major advances in the mathematical modelling of healthcare systems, in order to improve efficiency, and ultimately, patient health. The first expected outcome is the development of mathematical models that constitute a high-level description of patient flow through hospitals and subacute care, so that demands for emergency and elective capacity are met ....Modeling Healthcare Systems. An efficient healthcare system is essential for the well-being of any society. The aim of the project is to develop major advances in the mathematical modelling of healthcare systems, in order to improve efficiency, and ultimately, patient health. The first expected outcome is the development of mathematical models that constitute a high-level description of patient flow through hospitals and subacute care, so that demands for emergency and elective capacity are met given limited resources. The second is the development of a bed allocation algorithm that allocates patients to appropriate wards, so as to optimise the set of performance indicators of the system under appropriate constraints, given the current ward occupancy.Read moreRead less
Statistical methods for the analysis of critical care data, with application to the Australian and New Zealand Intensive Care Database. The recent inquiry into Queensland's Bundaberg Base Hospital highlights the need to monitor hospital performance. This project develops new statistical methods to account for uncertainty in the assessment of provider performance and its outcomes will provide government with institutional comparisons for policy and planning.
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
Mitigating bias in statistical analyses of data collected over time. This project aims to develop innovative nonparametric distribution and regression curve estimation techniques from data collected over time. These curves are key statistical tools for describing populations, but often, their estimators are inefficient when the data are massive, growing and change over time, or too restrictive when the data exhibit measurement errors and a fraction of them are equal to zero. The project expects ....Mitigating bias in statistical analyses of data collected over time. This project aims to develop innovative nonparametric distribution and regression curve estimation techniques from data collected over time. These curves are key statistical tools for describing populations, but often, their estimators are inefficient when the data are massive, growing and change over time, or too restrictive when the data exhibit measurement errors and a fraction of them are equal to zero. The project expects to develop novel, less restrictive and more realistic nonparametric curve estimation methods in these complex settings. Outcomes include new practical statistical methods and software to benefit experts in diverse fields from nutrition and epidemiology, to environmental science and digital platforms, amongst others.Read moreRead less