A Bayesian model for misclassified binary outcomes and correlated survival data with applications to breast cancer.
Journal Article (Journal Article)
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.
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Duke Authors
Cited Authors
- Luo, S; Yi, M; Huang, X; Hunt, KK
Published Date
- June 15, 2013
Published In
Volume / Issue
- 32 / 13
Start / End Page
- 2320 - 2334
PubMed ID
- 22996169
Pubmed Central ID
- 22996169
Electronic International Standard Serial Number (EISSN)
- 1097-0258
Digital Object Identifier (DOI)
- 10.1002/sim.5629
Language
- eng
Conference Location
- England