Enhancing Aspects Of Time-to-event Analysis Methodology In Randomised Trials
Funder
National Health and Medical Research Council
Funding Amount
$548,446.00
Summary
Time-to-event analysis is a statistical method for examining the occurrence of disease-related events in individuals followed for varying periods of time. The method is widely used in health research. The technicalities of the methods are subtle and by paying careful attention to these this grant will provide extended methods, new software, and apply methods more effectively to gain new insights to disease progress, and to enhance the efficiency of health research.
New Methods And Guidelines For The Design, Analysis And Reporting Of Cluster-crossover And Stepped Wedge Randomised Trials In Clinical And Public Health Research
Funder
National Health and Medical Research Council
Funding Amount
$391,258.00
Summary
Cluster crossover and stepped wedge designs have emerged recently as study designs used in clinical and public health research settings. However, despite their use there has been very little methodological assessment of the statistical analysis methods used in current practice. The proposed research will assess the correctness of current methods and will produce a toolkit of state-of-the art, defensible trial design, analysis and reporting methods for the clinical/health researcher.
Joint Longitudinal And Time-to-event Models For Applications In Health Research
Funder
National Health and Medical Research Council
Funding Amount
$86,073.00
Summary
A recently developed statistical technique ("joint modelling") allows for both repeatedly measured biomarker data (for example, blood pressure measurements) and event time data (for example, time until death) to be analysed together. There are several potential benefits to using these models, but since the methods are relatively new their uptake in applied health research remains limited. This PhD will consist of several distinct but interrelated projects which explore the use of these models.
Increasing the efficiency and interpretability of stepped wedge trials. Stepped wedge cluster randomised trials are increasingly being used to test interventions, across many disciplines. This project aims to develop highly efficient trial designs and new methods for the estimation of causally interpretable effects when adherence to interventions is not perfect. This project expects to generate new design types that reduce resources required to test interventions, and methods to understand how t ....Increasing the efficiency and interpretability of stepped wedge trials. Stepped wedge cluster randomised trials are increasingly being used to test interventions, across many disciplines. This project aims to develop highly efficient trial designs and new methods for the estimation of causally interpretable effects when adherence to interventions is not perfect. This project expects to generate new design types that reduce resources required to test interventions, and methods to understand how these interventions work. Expected outcomes include tools to help researchers develop cheaper and more appealing trials, tools to estimate causal effects, the methodology underpinning these tools, and new collaborations. This should provide significant benefits by allowing more interventions to be tested and understood.Read moreRead less
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.
Integration Of Biostatistics And Mathematical Modelling To Improve The Control Of Infectious Diseases
Funder
National Health and Medical Research Council
Funding Amount
$622,655.00
Summary
Improving the control of infectious diseases requires the evaluation of interventions that prevent disease at the population level and successfully treat infections at the individual level. This proposal brings together advanced biostatistical research with mathematical modelling to discover novel methods for evaluating antimalarial treatments and malaria vaccine candidates, leading to new insights in infectious disease control and building capacity in this emerging cross-disciplinary field.
Statistical Approaches For Studying The Safety And Effectiveness Of Medicines
Funder
National Health and Medical Research Council
Funding Amount
$307,946.00
Summary
When medicines first reach the market many of the potential adverse reactions are unknown. Clinical trials of medicines often exclude elderly patients and patients with multiple chronic diseases who are at higher risk of adverse drug reactions. This research aims to improve medicine safety by developing statistical methods to detect and validate medication safety signals and to identify patients who are at high risk of adverse reactions to medicines.
This research will develop cutting-edge computational tools and statistical methods to analyse, model and visualise the way in which the human brain is interconnected. The tools developed will be used to identify biological markers in the brain’s network of axonal circuitry (the connectome) that are valuable for diagnosis and prognosis of psychiatric disorders. This research will bring to fruition the exciting potential of connectomics in neuroscience and psychiatry.
Automated Screening Measures Associated With Risk And Treatment (SMART) Of Breast Cancer
Funder
National Health and Medical Research Council
Funding Amount
$98,244.00
Summary
Women with greater mammographic density (white area on a mammogram) are at greater risk of breast cancer. Prof Hopper (supervisor) has led international research in this area using a method called CUMULUS. Drs Makalic and Schmidt (co-supervisors) have created an automated measure, called CIRRUS. My aims are to: find out which factors influence CIRRUS, confirm that CIRRUS predicts breast cancer risk, and develop automated measures of a breast cancer risk based on magnetic resonance imaging (MRI).