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

  • Informative censoring

• Graduation date

  Fall 2009

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R35S4V

• 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.