Informative Random Censoring in Parametric Survival Models

  • Author / Creator
    Li, Weihong
  • Informative random censoring survival data are often seen in clinical trials. However, the methodology to deal with this kind of data has not been well developed due to difficulty of identifying the information. Several methods were proposed, for example, by \citet{Sia1}. We use simulation studies to investigate sensitivity of these methods and show that the maximum likelihood estimation (MLE) method provides narrower confidence intervals than \citet{Sia1}. This is true and expected under the same assumption as in \citet{Sia1}. However, we were able to give practical guidelines on how to guess at the missing information of random censoring. We give conditions to obtain more precise estimators for survival data analyses, providing a user-friendly R program. Two real-life data sets are used to illustrate the application of this methodology.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Mathematical and Statistical Sciences
  • Supervisor / co-supervisor and their department(s)
    • Keumhee Carriere Chough, Department of Mathematical and Statistical Sciences
  • Examining committee members and their departments
    • A "Sentil" Senthilselvan, Public Health Sciences
    • Narasimha Prasad, Department of Mathematical and Statistical Sciences