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

Publication ,  Journal Article
Luo, S; Yi, M; Huang, X; Hunt, KK
Published in: Stat Med
June 15, 2013

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.

Duke Scholars

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

June 15, 2013

Volume

32

Issue

13

Start / End Page

2320 / 2334

Location

England

Related Subject Headings

  • Statistics & Probability
  • Neoplasms, Second Primary
  • Neoplasm Recurrence, Local
  • Monte Carlo Method
  • Models, Statistical
  • Markov Chains
  • Kaplan-Meier Estimate
  • Humans
  • Female
  • Diagnostic Tests, Routine
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Luo, S., Yi, M., Huang, X., & Hunt, K. K. (2013). A Bayesian model for misclassified binary outcomes and correlated survival data with applications to breast cancer. Stat Med, 32(13), 2320–2334. https://doi.org/10.1002/sim.5629
Luo, Sheng, Min Yi, Xuelin Huang, and Kelly K. Hunt. “A Bayesian model for misclassified binary outcomes and correlated survival data with applications to breast cancer.Stat Med 32, no. 13 (June 15, 2013): 2320–34. https://doi.org/10.1002/sim.5629.
Luo, Sheng, et al. “A Bayesian model for misclassified binary outcomes and correlated survival data with applications to breast cancer.Stat Med, vol. 32, no. 13, June 2013, pp. 2320–34. Pubmed, doi:10.1002/sim.5629.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

June 15, 2013

Volume

32

Issue

13

Start / End Page

2320 / 2334

Location

England

Related Subject Headings

  • Statistics & Probability
  • Neoplasms, Second Primary
  • Neoplasm Recurrence, Local
  • Monte Carlo Method
  • Models, Statistical
  • Markov Chains
  • Kaplan-Meier Estimate
  • Humans
  • Female
  • Diagnostic Tests, Routine