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).
A Program Of Methodological And Collaborative Research In Biostatistics And Population Health
Funder
National Health and Medical Research Council
Funding Amount
$264,081.00
Summary
Biostatistics is a critical component of health and medical research, especially for studies in population health. However, there is an increasing gap between supply and demand for high-level biostatistical input. This proposal combines novel methodological research into methods for analysing incomplete data, with collaborative research applying new ideas and complex analyses to important health problems. The fellowship will facilitate my development as a future leader in this key area.
New genomic technologies are revolutionizing biological research. RNA-seq is a recently developed high-throughput sequencing technology that provides scientists with much more detail how genes are regulated and expressed than any earlier technology. New tools developed by Professor Gordon Smyth are allowing researchers to use RNA-Seq technology to more accurately determine which genes are genuinely changing in the development of cancers and in response to cancer treatments.
Diagnostics For Mixture Regression Models: Applications To Public Health
Funder
National Health and Medical Research Council
Funding Amount
$128,250.00
Summary
In many public health studies, finite mixture regression models are often used to analyse data arising from heterogeneous populations. It is important to assess the stability of parameter estimates and the validity of statistical inferences when the underlying assumptions appear to be violated, but appropriate diagnostics are lacking in the literature. This research aims to develop effective diagnostic methods for assessing the adequacy of mixture regression models and the sensitivity of accompa ....In many public health studies, finite mixture regression models are often used to analyse data arising from heterogeneous populations. It is important to assess the stability of parameter estimates and the validity of statistical inferences when the underlying assumptions appear to be violated, but appropriate diagnostics are lacking in the literature. This research aims to develop effective diagnostic methods for assessing the adequacy of mixture regression models and the sensitivity of accompanying test statistics. The methodology developed will enable health care professionals to focus on substantive issues and to draw accurate and valid conclusions inferred from correlated and over-dispersed outcomes. In the presence of anomalous observations, the influence diagnostics can provide insights into the source of heterogeneity and the apparent over-dispersion, while accommodating the inherent correlation due to the longitudinal study design or nested data structure. Significance of the research lies in its scientific novelty and the breadth of its practical applications. The benefits to public health will accrue both nationally and internationally. For the empirical studies that motivated and are linked to this research, evaluation of health outcomes has significant implications in the prevention and control of recurrent urinary tract infections, hospital strategic planning, and post-stroke care and rehabilitation management. Moreover, appropriate assessment of a physical activity intervention for older adults is pertinent to falls prevention and reduction of musculoskeletal disorders among sedentary seniors.Read moreRead less
Hierarchical Finite Mixture Modelling Of Health Outcomes: A Risk-adjusted Random Effects Approach
Funder
National Health and Medical Research Council
Funding Amount
$117,000.00
Summary
In medical and health studies, finite mixture regression models have been used to analyze data arising from heterogeneous populations. Traditionally, the application of mixture models is mainly concerned with finite normal mixtures. Recent computational advances and methodological developments have enhanced the extension of the method to non-normal finite mixtures, such as the modelling of discrete responses in finite mixture of generalized linear models and overlapping phases of failure time da ....In medical and health studies, finite mixture regression models have been used to analyze data arising from heterogeneous populations. Traditionally, the application of mixture models is mainly concerned with finite normal mixtures. Recent computational advances and methodological developments have enhanced the extension of the method to non-normal finite mixtures, such as the modelling of discrete responses in finite mixture of generalized linear models and overlapping phases of failure time data in the context of survival analysis. However, due to the hierarchical study design or the data collection procedure, the inherent correlation structure and-or clustering effects present may contribute to extra variations and violation of the independence assumption, resulting in spurious associations and misleading inferences based on the finite mixture model. This project aims to present a unified approach to accommodate both heterogeneity and dependency of observations, by incorporating random effects into finite mixture regression models. The new methodology will provide an integrated framework to analyze heterogeneous and correlated health outcomes. Three empirical studies are considered, namely, evaluation of an occupational injury reduction intervention, length of hospital stay modeling, and analysis of survival times of patients after cardiac surgery. The long term benefits to bioscience are accurate and valid conclusions inferred from medical and health studies, as well as the correct identification of high-risk subgroups. For the three application areas of this project, the improved analyses will specifically enable the evaluation of a participatory ergonomics intervention, the assessment of hospital efficiency and factors influencing length of hospitalization, and the determination of effectiveness of treatments prescribed pre- and post- operation, respectively.Read moreRead less
Development And Evaluation Of Statistical Methods And Software For Analysis Of Complex Genetic Disease Data
Funder
National Health and Medical Research Council
Funding Amount
$1,250,371.00
Summary
What are the major factors underpinning complex genetic diseases like diabetes, bipolar disorder or cancer? To answer this question new tools are needed, including software for mining the human genome with interactions between the genome and environment being incorporated. This is our focus. It will form the basis of a superior understanding of the overall process leading to disease and hence better predictions with important ramifications for new treatments and health care planning.
This project aims to establish agreement on appropriate methods to analyse evidence in support of medical treatments directed at laboratory tests (such as blood cholesterol) and to classify the evidence according to how convincing it is. The goal of those developing new drugs targeting one of these laboratory tests is to have their evidence sufficiently convincing that the drug will be approved for sale and used because doctors and patients believe its use will translate into a patient benefit.
Statistical Methods For Handling Missing Data In Longitudinal Studies
Funder
National Health and Medical Research Council
Funding Amount
$198,000.00
Summary
Modern epidemiological research has a strong focus on studying the causes and consequences of major health outcomes over the life span. Studies are increasingly conducted on large cohorts of individuals over long periods of time, extending from before birth through to the later years of life. An example of this type of study is the Victorian Adolescent Health Cohort Study, which began in 1992 with participants aged 15 and is now seeking funding for a 9th wave of data collection in 2005. A major ....Modern epidemiological research has a strong focus on studying the causes and consequences of major health outcomes over the life span. Studies are increasingly conducted on large cohorts of individuals over long periods of time, extending from before birth through to the later years of life. An example of this type of study is the Victorian Adolescent Health Cohort Study, which began in 1992 with participants aged 15 and is now seeking funding for a 9th wave of data collection in 2005. A major challenge that arises in analysing data from studies of this kind is the difficulty created by the occurrence of missing data. In longitudinal studies with multiple measurement occasions, participants rarely complete all waves of data collection, and even when present an individual may not provide data on all study variables. Common practice in analysing such data is to omit individuals entirely if they have a missing value on any of the variables required for the analysis in question. This approach can lead to major biases in conclusions, by excluding individuals in whom patterns of association may be quite different than among those retained, and at best leads to loss of reliability in findings due to the reduction in numbers available for analysis. Recent statistical research has led to a range of new techniques for better handling of missing data in such studies, including the method of multiple imputation (MI), under which multiple copies of the dataset are created with imputed values filled in for the missing values. This approach has enormous potential for helping to produce better answers from large longitudinal studies but a number of issues require research to ensure that the method is made available to researchers in a convenient form and, most importantly, used in a way that leads to sound conclusions. This project will address many of these issues, leading to enhanced capacity to extract valuable information from large epidemiological studies.Read moreRead less
New Developments for Bayesian statistical models and computational methods. Bayesian methods of statistical analysis provide a flexible theory for addressing inference in the presence of uncertainty. Consequently Bayesian methods have enabled scientific discovery in areas characterised as complex systems where new developments in modelling and computational methods have been crucial. Significant barriers to further success involve challenges in formulating and validating models, dealing with l ....New Developments for Bayesian statistical models and computational methods. Bayesian methods of statistical analysis provide a flexible theory for addressing inference in the presence of uncertainty. Consequently Bayesian methods have enabled scientific discovery in areas characterised as complex systems where new developments in modelling and computational methods have been crucial. Significant barriers to further success involve challenges in formulating and validating models, dealing with large data sets, and developing efficient computational methods. The principal aim of this project is to develop new Bayesian modelling and computational methodology which address these challenges with broad application.Read moreRead less