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. / Biostatistics
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/651 |
| Date | 11 1900 |
| Creators | Li, Weihong |
| Contributors | Keumhee Carriere Chough, Department of Mathematical and Statistical Sciences, Narasimha Prasad, Department of Mathematical and Statistical Sciences, A "Sentil" Senthilselvan, Public Health Sciences |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 1044451 bytes, application/pdf |
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