title: 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 article, 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 dataset consisting of 4, 477 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.
authors.affiliation: Division of Biostatistics, The University of Texas Health Science Center at Houston, 1200 Pressler St, Houston, TX 77030, USA ( sheng.t.luo@uth.tmc.edu ;
authors.forename: Sheng
authors.initials: S
authors.lastname: Luo
authors.affiliation: Division of Biostatistics, The University of Texas Health Science Center at Houston.
authors.forename: Xiao
authors.initials: X
authors.lastname: Su
authors.affiliation: Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Texas 77030, USA.
authors.forename: Min
authors.initials: M
authors.lastname: Yi
authors.affiliation: Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Texas 77030, USA.
authors.forename: Kelly K
authors.initials: KK
authors.lastname: Hunt
keywords: Binary outcome; Diagnostic error; Imperfect tests; Latent class model; Sensitivity; Specificity
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