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Bayesian nonparametric inference on quantile residual life function: Application to breast cancer data

Authors
Park, TaeyoungJeong, Jong-HyeonLee, Jae Won
Issue Date
15-8월-2012
Publisher
WILEY-BLACKWELL
Keywords
blocked Gibbs sampler; heavily censored survival data; median residual life function; partially collapsed Gibbs sampler; survival analysis
Citation
STATISTICS IN MEDICINE, v.31, no.18, pp.1972 - 1985
Indexed
SCIE
SCOPUS
Journal Title
STATISTICS IN MEDICINE
Volume
31
Number
18
Start Page
1972
End Page
1985
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/107704
DOI
10.1002/sim.5353
ISSN
0277-6715
Abstract
There is often an interest in estimating a residual life function as a summary measure of survival data. For ease in presentation of the potential therapeutic effect of a new drug, investigators may summarize survival data in terms of the remaining life years of patients. Under heavy right censoring, however, some reasonably high quantiles (e.g., median) of a residual lifetime distribution cannot be always estimated via a popular nonparametric approach on the basis of the KaplanMeier estimator. To overcome the difficulties in dealing with heavily censored survival data, this paper develops a Bayesian nonparametric approach that takes advantage of a fully model-based but highly flexible probabilistic framework. We use a Dirichlet process mixture of Weibull distributions to avoid strong parametric assumptions on the unknown failure time distribution, making it possible to estimate any quantile residual life function under heavy censoring. Posterior computation through Markov chain Monte Carlo is straightforward and efficient because of conjugacy properties and partial collapse. We illustrate the proposed methods by using both simulated data and heavily censored survival data from a recent breast cancer clinical trial conducted by the National Surgical Adjuvant Breast and Bowel Project. Copyright (c) 2012 John Wiley & Sons, Ltd.
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