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Comprehensive risk stratification model for prognostication and assisting with therapeutic decision-making for multiple myeloma patients

Author: Brian Song
Publisher: [Long Beach, California] : California State University, Long Beach, 2018.
Dissertation: M.S. California State University, Long Beach 2018
Series: California State University, Long Beach.; Master's thesis collection, Department of Mathematics and Statistics.
Edition/Format:   Thesis/dissertation : Thesis/dissertation : eBook   Computer File : English
Summary:
Abstract: 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  Read more...
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Material Type: Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Brian Song
ISBN: 9780438538849 0438538846
OCLC Number: 1083764877
Description: 1 online resource (vi, 42 pages) : color illustrations.
Series Title: California State University, Long Beach.; Master's thesis collection, Department of Mathematics and Statistics.
Responsibility: by Brian Song.

Abstract:

Abstract: 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.

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