Disentangling The Interrelationship Between Multimordibity, Multimedicine Use, And Cardiovascular Health
Funder
National Health and Medical Research Council
Funding Amount
$480,978.00
Summary
Australians are living longer, but are also living with more health conditions and taking more medicines to treat those conditions. For people with cardiovascular disease (CVD), this is a problem as hundreds of non-cardiac medicines known to increase the risk of cardiovascular events, such as myocardial infarction, stroke, or heart failure. We will take a holistic, patient-centred approach determine the true burden of CVD related to use of medicines to treat comorbid conditions in Australia.
Radiotherapy Treatment For Prostate Cancer - A Change In Practice Based On Direct Evidence For Targeting And Toxicity Effects Using Real Outcomes Data
Funder
National Health and Medical Research Council
Funding Amount
$555,129.00
Summary
Radiotherapy for prostate cancer treatment will be more effective when we have better knowledge of what patient anatomy needs to be targeted, and what needs to be avoided. This project will combine data collected during a large Australasian prostate cancer radiotherapy trial, ‘RADAR’, with data collected using new patient imaging methods to determine how patient anatomy impacts on the effectiveness of their treatment and the side-effects they experience.
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.
Optimising Radiation Therapy Delivery For Cancer Patients Using Daily Image Guidance To Maximize Cure And Reduce Normal Tissue Side Effects
Funder
National Health and Medical Research Council
Funding Amount
$510,968.00
Summary
When using radiotherapy to kill tumours, the radiation beams need to be targeted at the tumour, plus a margin of error around it to ensure that it receives sufficient dose despite uncertainties in its exact location relative to reference points used for beam alignment. Advanced statistical modelling techniques applied to data collected from patients will be used to determine the optimal margin width for individual patients to maximise cancer cure while minimising normal tissue side effects.
Using Big Data To Reduce Inappropriate Medication Use
Funder
National Health and Medical Research Council
Funding Amount
$318,768.00
Summary
Potentially inappropriate medication use both increases patient harm and wastes considerable health resources. However methods for measuring patterns of use are not well developed nor utilised in policy. This research will measure the scope, variation and burden of potentially inappropriate medication use in Australia. My unique combination of biostatistical, data and policy expertise will enable this research to create new actionable tools for evaluating the Australian healthcare system.
ADVANCING THE EVIDENCE BASE FOR CARE AND POLICY IN PRIORITY HEALTH AREAS
Funder
National Health and Medical Research Council
Funding Amount
$11,195,727.00
Summary
This program will improve health care and policy through clinical trials research and better methods for combining trial evidence. The team will tackle priority health areas to reduce death and serious disability: in particular in cancer, cardiovascular disease, diabetes, obesity and neonatal diseases. The program team includes clinicians, epidemiologists, trialists, biostatisticians, and health economists and collaborative networks of clinical investigators in each disease area.
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.
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