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A Bayesian model for misclassified binary outcomes and correlated survival data with applications to breast cancer
Author(s) -
Luo Sheng,
Yi Min,
Huang Xuelin,
Hunt Kelly K.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5629
Subject(s) - covariate , bayesian probability , breast cancer , bayesian inference , computer science , markov chain monte carlo , medicine , oncology , cancer , artificial intelligence , machine learning
Breast cancer patients may experience ipsilateral breast tumor relapse (IBTR) after breast conservation therapy. IBTR is classified as either true local recurrence or new ipsilateral primary tumor. The correct classification of IBTR status has significant implications in therapeutic decision‐making and patient management. However, the diagnostic tests to classify IBTR are imperfect and prone to misclassification. In addition, some observed survival data (e.g., time to relapse, time from relapse to death) are strongly correlated with IBTR status. We present a Bayesian approach to model the potentially misclassified IBTR status and the correlated survival information. We conduct the inference using a Bayesian framework via Markov chain Monte Carlo simulation implemented in WinBUGS . Extensive simulation shows that the proposed method corrects biases and provides more efficient estimates for the covariate effects on the probability of IBTR and the diagnostic test accuracy. Moreover, our method provides useful subject‐specific patient prognostic information. Our method is motivated by, and applied to, a dataset of 397 breast cancer patients. Copyright © 2012 John Wiley & Sons, Ltd.