Simultaneous inference of a misclassified outcome and competing risks failure time data

Abstract

Ipsilateral breast tumor relapse (IBTR) often occurs in breast cancer patients after their breast conservation therapy. The IBTR status' classification (true local recurrence versus new ipsilateral primary tumor) is subject to error and there is no widely accepted gold standard. Time to IBTR is likely informative for IBTR classification because new primary tumor tends to have a longer mean time to IBTR and is associated with improved survival as compared with the true local recurrence tumor. Moreover, some patients may die from breast cancer or other causes in a competing risk scenario during the follow-up period. Because the time to death can be correlated to the unobserved true IBTR status and time to IBTR (if relapse occurs), this terminal mechanism is non-ignorable. In this paper, we propose a unified framework that addresses these issues simultaneously by modeling the misclassified binary outcome without a gold standard and the correlated time to IBTR, subject to dependent competing terminal events. We evaluate the proposed framework by a simulation study and apply it to a real data set consisting of 4477 breast cancer patients. The adaptive Gaussian quadrature tools in SAS procedure NLMIXED can be conveniently used to fit the proposed model. We expect to see broad applications of our model in other studies with a similar data structure.

Keywords:

• diagnostic error
• imperfect tests
• binary outcome
• latent class model
• sensitivity
• specificity

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors thank the editor and two anonymous referees for their reading and valuable comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed at http://dx.doi.org/10.1080/02664763.2014.995606.

Additional information

Funding

The project described was supported by the National Center for Research Resources; the National Center for Advancing Translational Sciences, National Institutes of Health [grant number KL2 TR000370].