The goal of the research is to improve current risk stratification models of multiple myeloma by developing a novel statistical decision algorithm. The increase in precision would assist in providing optimal treatments for multiple myeloma cancer patients depending on the risk of progression at the time of diagnosis. If progression of cancer is imminent, then risk-adapted therapy would be a considerable option. Larger amount of data supplied from multiple clinics were gathered to obtain better prognosis. The data are available from the Synapse website under the Multiple Myeloma DREAM Challenge site. Although both genomic variation data and gene expression data were available, the study was done with the latter in conjunction with general patient data. Preliminary research has shown that the microarray data were not standardized among the different clinics, so the study required additional preprocessing before aggregating all data for comprehensive investigation. Accelerated Time Failure model is used to screen insignificant variables for easier processing, reducing 17,308 markers to 4,503. A combination of random forest models and likelihood ratio test is utilized to further reduce potentially significant biomarkers. The remaining biomarkers are used in multiple statistical models to determine the optimal model that best represents the data. The efficacy of the model is checked by using two clinics to train the model to predict the third clinic. The average and standard deviation of the resulting statistics are used to validate the consistency of the model for different clinics. We show that an improvement in current risk stratification models can be obtained.
|Commitee:||Suaray, Kagba, Zhou, Tianni|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 58/02M(E), Masters Abstracts International|
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