Discovery Early Career Researcher Award - Grant ID: DE180101520
Funder
Australian Research Council
Funding Amount
$365,058.00
Summary
Diet, variance and individual variability in life-history. This project aims to provide biologists with novel statistical tools that will shift analytical paradigms. In many species, dietary restrictions increase average lifespan, and affect average rates of growth and reproduction, also known as ‘life history’. The use of recently developed tools has shown that individual variability in life history also appears to increase under dietary restrictions. This project will explore the effects of di ....Diet, variance and individual variability in life-history. This project aims to provide biologists with novel statistical tools that will shift analytical paradigms. In many species, dietary restrictions increase average lifespan, and affect average rates of growth and reproduction, also known as ‘life history’. The use of recently developed tools has shown that individual variability in life history also appears to increase under dietary restrictions. This project will explore the effects of diet composition on variability in life-history traits, and the factors driving this variation. This is expected to improve the prediction of the effects of changing nutritional environments.Read moreRead less
High Predictive Performance Models via Semi-Parametric Survival Regression. This project will develop novel statistical models for high prediction performance. When applied to help doctor to treat patients, these models allow the users to include gene or other biomarkers for predicting effectiveness of a treatment. When applied to risk management in finance, these models are capable to include an organization's or individual's ongoing finance status to predict, for example, the probability of or ....High Predictive Performance Models via Semi-Parametric Survival Regression. This project will develop novel statistical models for high prediction performance. When applied to help doctor to treat patients, these models allow the users to include gene or other biomarkers for predicting effectiveness of a treatment. When applied to risk management in finance, these models are capable to include an organization's or individual's ongoing finance status to predict, for example, the probability of or time to loan default. Innovative computational methods will be developed for fitting these models. Compared to traditional prediction method, this approach allows greater flexibility while being superior in terms of statistical accuracy and bias. Extensive analyses of healthcare data from diverse fields will be undertaken.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.
Post-market Surveillance Of Medicine-related Adverse Events
Funder
National Health and Medical Research Council
Funding Amount
$99,248.00
Summary
Observational studies using administrative data are an important complement to spontaneous reporting systems for detecting medicine-related adverse events after they go to market, as they reflect real-world use of medicines; yet, they require rigorous methodological approaches to avoid bias. This project will review the existing methodologies for detecting adverse events in administrative data and apply them to Australian data.
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.