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A Bayesian model for misclassified binary outcomes and correlated survival data with applications to breast cancer

Authors


  • Supporting information may be found in the online version of this article.
  • Sheng Luo and Min Yi contributed equally to this work.

Correspondence to: Sheng Luo, Division of Biostatistics, University of Texas School of Public Health, 1200 Pressler St, Houston, Texas 77030, U.S.A.

E-mail: sheng.t.luo@uth.tmc.edu

Abstract

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.

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