University of SaskatchewanHARVEST
  • Login
  • Submit Your Work
  • About
    • About HARVEST
    • Guidelines
    • Browse
      • All of HARVEST
      • Communities & Collections
      • By Issue Date
      • Authors
      • Titles
      • Subjects
      • This Collection
      • By Issue Date
      • Authors
      • Titles
      • Subjects
    • My Account
      • Login
      JavaScript is disabled for your browser. Some features of this site may not work without it.
      View Item 
      • HARVEST
      • Electronic Theses and Dissertations
      • Graduate Theses and Dissertations
      • View Item
      • HARVEST
      • Electronic Theses and Dissertations
      • Graduate Theses and Dissertations
      • View Item

      Randomized Survival Probability Residual for Assessing Parametric Survival Models

      Thumbnail
      View/Open
      WU-THESIS-2018.pdf (1.219Mb)
      Date
      2018-12-21
      Author
      Wu, Tingxuan 1987-
      Type
      Thesis
      Degree Level
      Masters
      Metadata
      Show full item record
      Abstract
      Traditional residuals for diagnosing accelerated failure time models in survival analysis, such as Cox-Snell, martingale and deviance residuals, have been widely used. However, ex- amining those residuals are often only made visually, which can be subjective. Therefore, lack of objective measure of examining model adequacy has been a long-standing issue that needs to be addressed for survival analysis. In this thesis, a new type of residual is proposed called Normal-transformed Randomized Survival Probability (NRSP) residual. A compre- hensive review of the traditional residuals including Cox Snell and deviance residuals is firstly presented highlighting their disadvantages for examining model adequacy. We then introduce NRSP residual. Simulation studies were conducted to compare the performance of NRSP residuals with the traditional residuals. Our simulation studies demonstrated that NRSP residuals are approximately normally distributed when the fitted model is correctly speci- fied, and has great statistical power in detecting model inadequacies. We also apply NRSP residuals to a real dataset to check the goodness-of-fit of three plausible models.
      Degree
      Master of Science (M.Sc.)
      Department
      School of Public Health
      Program
      Biostatistics
      Committee
      Li, Longhai; Feng, Cindy; Khan, Shahedul; Pahwa, Punam; Shao, Enchuan
      Copyright Date
      December 2018
      URI
      http://hdl.handle.net/10388/11696
      Subject
      RSP
      UCS
      MCS
      NCS
      Collections
      • Graduate Theses and Dissertations
      University of Saskatchewan

      University Library

      © University of Saskatchewan
      Contact Us | Disclaimer | Privacy