Development Of Statistical Methodologies And Application To Clinical Cancer Studies
Funder
National Health and Medical Research Council
Funding Amount
$428,065.00
Summary
Integrating different layers of information coming from the recent ‘-omics’ technologies can help improving the treatment and the prevention of complex diseases. In particular, the identification of molecular markers of different types can be used for better diagnostics and prognosis in cancer and immune diseases. This project will develop innovative statistical solutions to handle and make sense of the vast amount of biological data that are routinely generated in the laboratories.
Analysis Of HIV Virologic Response-rebound Data: Prognostic Indicators Of Post-HAART Viral Control
Funder
National Health and Medical Research Council
Funding Amount
$144,000.00
Summary
The introduction of potent anti-retroviral therapy into standard clinical management of HIV infected individuals has been associated with high rates of reduction in plasma viral loads over short periods of time. However, there remains considerable variation in the degree of longer-term viral control as a result of viral resistance, toxicity, timing of treatment initiation and choice of drug regimen. In particular, the most appropriate time to initiate treatment remains clouded, with the need to ....The introduction of potent anti-retroviral therapy into standard clinical management of HIV infected individuals has been associated with high rates of reduction in plasma viral loads over short periods of time. However, there remains considerable variation in the degree of longer-term viral control as a result of viral resistance, toxicity, timing of treatment initiation and choice of drug regimen. In particular, the most appropriate time to initiate treatment remains clouded, with the need to initiate treatment sufficiently early in order to avoid irreversible damage balanced by the problems of potential viral resistance or toxicity if started too soon. Determination of factors which will assist practitioners to optimise the timing of treatment initiation remains a high priority. Our aim in this project is to develop and study the use of novel statistical mixed-effects models designed to analyse factors associated with visit-time viral load data following commencement of therapy, taking account of the entire follow-up profiles of responses over time. The project involves both theoretical and empirical analyses of the estimation and inferential properties of the mixed-model method in conjunction with comprehensive analyses of prognostic factors associated with post-treatment virologic control in patients from the Western Australian HIV Cohort Study. These include demographic, virologic, immunologic, adherence and host genetic factors. The statistical methods developed will have wide applicability and add significantly to the suite of procedures available for the analysis of longitudinal response data.Read moreRead less
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