A simulation‐extrapolation approach for regression analysis of misclassified current status data with the additive hazards model

• Published in: Statistics in medicine Vol. 40; no. 28; pp. 6309 - 6320
• Main Authors: Li, Shuwei, Tian, Tian, Hu, Tao, Sun, Jianguo
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 10.12.2021
• Subjects: Bias; Computer Simulation; Humans; interval censoring; misclassification; Proportional Hazards Models; Regression Analysis; SIMEX; Simulation; SIMEX; interval censoring; regression analysis; misclassification
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.9184

Current status data arise when each subject is observed only once and the failure time of interest is only known to be either smaller or larger than the observation time rather than observed exactly. For the situation, due to the use of imperfect diagnostic tests, the failure status could often suffer misclassification or one observes misclassified data, which may result in severely biased estimation if not taken into account. In this article, we discuss regression analysis of such misclassified current status data arising from the additive hazards model, and a simulation‐extrapolation (SIMEX) approach is developed for the estimation. Furthermore, the asymptotic properties of the proposed estimators are established, and a simulation study is conducted to assess the empirical performance of the method, which indicates that the proposed procedure performs well. In particular, it can correct the estimation bias given by the naive method that ignores the existence of misclassification. An application to a medical study on gonorrhea is also provided.