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

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.

Figure 1

Time plot of one patient. t₁ is the time from BCT to IBTR and t₂ is time from IBTR to cause-specific death or censoring

Figure 2

Kaplan-Meier curves displaying differences in time to relapse (left panels) and time from IBTR to death (right panels) for patients classified as having new primary tumors (NP) and true recurrences (TR) by test 1 (top panels) and test 2 (bottom panels).