KUKU

KU ScholarWorks

  • myKU
  • Email
  • Enroll & Pay
  • KU Directory
    • Login
    View Item 
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Novel Statistical Methodology Development and Applications in ALS Research

    Thumbnail
    View/Open
    Karanevich_ku_0099D_15643_DATA_1.pdf (1.058Mb)
    Issue Date
    2017-12-31
    Author
    Karanevich, Alex George
    Publisher
    University of Kansas
    Format
    94 pages
    Type
    Dissertation
    Degree Level
    Ph.D.
    Discipline
    Biostatistics
    Rights
    Copyright held by the author.
    Metadata
    Show full item record
    Abstract
    Being able to predict, with accuracy, the disease progression of patients with a given disease is extremely useful from the perspectives of clinicians, patients, and clinical trial investigators. We introduce a novel method of reducing the expected prediction error when using linear models, given approximate monotonicity of the response; we refer to this method as utilizing an “anchor.” We justify this method mathematically, and then show how to improve predictions arising from standard ordinary least squares (OLS) models when modelling disease progression in a population of patients with amyotrophic lateral sclerosis (ALS). We go on to show that using an anchor can be used in conjunction with more complex modelling schemes to further improve the predictions of ALS patients; an anchor improves both Bayesian hierarchical linear models and Bayesian mixture models. Furthermore, we explore potential covariates that may be included in the models to improve predictions, but find that only time of disease onset results in improved model performance. We also explore how well these models work in a clinical setting, rather than in a clinical trial. We first demonstrate the feasibility of automatically extracting patients’ data, pertaining to survival and disease progression, from the electronic medical record, as well as showing that our disease progression model is feasible for clinical patients. We then compare survival rates between the two populations and determine that, even after adjusting for several important covariates, there is a large difference between survival in the clinic setting and survival in ALS clinical trials. We assert that the two patient groups’ differences in disease progression and survival highlight the needs to understand better disease variability in the clinical setting and to refine the inclusion criteria in ALS trials. We determine an anchor can be used to improve predictive models in ALS disease progression, for both simple independent OLS regressions and for far more complicated Bayesian hierarchical linear models. We conclude that using a Bayesian hierarchical linear model with an anchor is useful in both a clinical trial population of ALS patients as well as a dissimilar population seen in the Midwestern academic medical center ALS clinic.
    URI
    http://hdl.handle.net/1808/27027
    Collections
    • KU Med Center Dissertations and Theses [464]
    • Dissertations [4321]

    Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.


    We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.


    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    Browse

    All of KU ScholarWorksCommunities & CollectionsThis Collection

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    The University of Kansas
      Contact KU ScholarWorks
    Lawrence, KS | Maps
     
    • Academics
    • Admission
    • Alumni
    • Athletics
    • Campuses
    • Giving
    • Jobs

    The University of Kansas prohibits discrimination on the basis of race, color, ethnicity, religion, sex, national origin, age, ancestry, disability, status as a veteran, sexual orientation, marital status, parental status, gender identity, gender expression and genetic information in the University’s programs and activities. The following person has been designated to handle inquiries regarding the non-discrimination policies: Director of the Office of Institutional Opportunity and Access, IOA@ku.edu, 1246 W. Campus Road, Room 153A, Lawrence, KS, 66045, (785)864-6414, 711 TTY.

     Contact KU
    Lawrence, KS | Maps