This research is for the development of new approaches to the analysis of data from large cohort studies, either epidemiologic or clinical trials, with many qualitatively different variables observed over several time points, with exploratory genetic marker data vectors of length the order of thousands, with pedigree information, and with multiple correlated outcomes of interest.
The aim i s to develop methods for discovering relevant components or patterns of components in long or ultra-long genetic marker data vectors as they interact in conjunction with patterns or clusters of clinical/lifestyle/environmental variables and, possibly, pedigrees to suggest unusually high risk and correlations of interest for multiple outcomes of interest. The methods proposed are an attempt to develop tools that merge so-called data mining approaches and penalized likelihood methods (some developed in prior grants) and that have the ability to generate hypotheses which can then be examined more closely by classical parametric statistical methods and by experimenters to formulate further hypotheses. The emphasis is on approaches that have the ability to contribute information to evidence-based and personalized medical decision making. Data from Beaver Dam Eye Study will be used to examine the models under study for their reasonableness and for their ability to answer questions meaningful to the study scientists. The results will have broad applicability to other large epidemiological studies as well as to clinical trials, in particular those collecting local and genome-wide genetic marker information along with other, heterogenous risk factors.
Epidemiological and clinical studies take much of the credit for the dramatic improvement in public health and longevity in the last fifty years or so. Better understanding of the effect of lifestyle factors, clinical variables, treatment opportunities, and, more recently, genetic factors has come about as the result of straightforward as well as sophisticated analysis of the data gleaned from these studies. With extensive data collection and complex data structures, as well as improved computational and software resources, there are opportunities to further develop and extend modern data analysis methods to better capture complex relations between variables that affect outcomes of important personal and public health interest. It is proposed to exploit these opportunities.
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|Kong, Jing; Wang, Sijian; Wahba, Grace (2015) Using distance covariance for improved variable selection with application to learning genetic risk models. Stat Med 34:1708-20|
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|Lee, Yoonkyung; Lee, Cheol-Koo (2003) Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 19:1132-9|
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