Regression Analysis of Misclassified Current Status Data with Informative Observation Times

• Published in: Journal of systems science and complexity Vol. 36; no. 3; pp. 1250 - 1264
• Main Authors: Wang, Wenshan, Xu, Da, Zhao, Shishun, Sun, Jianguo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023; Springer Nature B.V
• Subjects: Algorithms; Asymptotic properties; Complex Systems; Control; Failure times; Mathematics; Mathematics and Statistics; Mathematics of Computing; Operations Research/Decision Theory; Regression analysis; Statistics; Systems Theory; Current status data; informative censoring; proportional hazard model; EM algorithm; misclassification
• ISSN: 1009-6124; 1559-7067
• DOI: 10.1007/s11424-023-2411-6

Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity. For the situation, another issue that may occur is that the observation time may be correlated with the interested failure time, which is often referred to as informative censoring or observation times. It is well-known that in the presence of informative censoring, the analysis that ignores it could yield biased or even misleading results. In this paper, the authors consider such data and propose a frailty-based inference procedure. In particular, an EM algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are established. The numerical results show that the proposed method works well in practice and an application to a set of real data is provided.