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Identify the Predictors of Damping by Model Selection and Regression Tree

Wei, Chi
http://orcid.org/0000-0001-9280-1910
http://rave.ohiolink.edu/etdc/view?acc_num=ucin163705447037351

Abstract Details

2021, MS, University of Cincinnati, Medicine: Biostatistics (Environmental Health).
Bone damping is a non-invasive measure of bone fragility and is identified as a better predictor of osteoporosis (OP) related bone fracture/fragility than bone mineral density (BMD). Subject with higher damping value demonstrates a heightened resistance to fracture. The purpose of this study was to identify the predictors of bone shock absorption (BSA) capacity measured as a damping factor by using model selection and multivariate multiple regression (MMR) method as well as regression tree. The main dataset was from an existing Cincinnati Lead Study (CLS) cohort. It is a prospective and longitudinal study examined early and late effects of childhood lead exposure on growth and development. The results of this study indicated that cortical vBMD, cortical thickness, endosteal circumference, cortical section modulus, current weight, and the number of pregnancies carried until the 3rd trimester were significant predictors of bone damping factor based on the method of model selection and MMR. Among the predictors with top nine highest variable importance values in regression tree method, four are the same as significant predictors from MMR analysis. Those are current weight, cortical section modulus, cortical vBMD, and endosteal circumference. Cortical section modulus and cortical vBMD have positive relationship with damping factor; however weight and endosteal circumference have negative relationship with damping factor. All variables’ relationships with damping factor are clinically significant. Lack of dataset from normal people to compare the differences and the missingness of the data are the limitation of the study. Current weight, cortical section modulus, cortical vBMD, and endosteal circumference are significant predictor of damping factor based on the study results. They are biologically relevant to damping and statistically significant in the damping model.
Amit Bhattacharya (Committee Member)
Marepalli Rao, Ph.D. (Committee Chair)
55 p.
Biostatistics
Model selection; Regression tree; Multivariate multiple regression; Damping; Bone shock absorption; Osteoporosis

Recommended Citations

Citations

  • Wei, C. (2021). Identify the Predictors of Damping by Model Selection and Regression Tree [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin163705447037351

    APA Style (7th edition)

  • Wei, Chi. Identify the Predictors of Damping by Model Selection and Regression Tree. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin163705447037351.

    MLA Style (8th edition)

  • Wei, Chi. "Identify the Predictors of Damping by Model Selection and Regression Tree." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin163705447037351

    Chicago Manual of Style (17th edition)

ucin163705447037351
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This open access ETD is published by University of Cincinnati and OhioLINK.