Table of Contents
International Scholarly Research Notices
Volume 2014 (2014), Article ID 430357, 9 pages
Research Article

Bayesian Perspective on Random Censored Survival Data

Department of Biostatistics, School of Public Health, University of Ghana, Legon, Accra, Ghana

Received 10 June 2014; Accepted 12 July 2014; Published 29 October 2014

Academic Editor: Francisco W. S. Lima

Copyright © 2014 Chris B. Guure and Samuel Bosomprah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A unit is said to be randomly censored when the information on time occurrence of an event is not available due to either loss to followup, withdrawal, or nonoccurrence of the outcome event before the end of the study. It is assumed in independent random/noninformative censoring that each individual has his/her own failure time and censoring time ; however, one can only observe the random vector, say, . The classical approach is considered for analysing the generalised exponential distribution with random or noninformative censored samples which occur most often in biological or medical studies. The Bayes methods are also considered via a numerical approximation suggested by Lindley in 1980 and that of the Laplace approximation procedure developed by Tierney and Kadane in 1986 with assumed informative priors alongside linear exponential loss function and squared error loss function. A simulation study is carried out to compare the estimators proposed in this paper. Two datasets have also been illustrated.