Regression Analysis of Misclassified Current Status Data with Informative Observation Times  

作　　者：WANG Wenshan XU Da ZHAO Shishun SUN Jianguo 

机构地区：[1]Center for Applied Statistical Research,School of Mathematics,Jilin University,Changchun 130012,China [2]Key Laboratory of Applied Statistics of MOE and School of Mathematics and Statistics,Northeast Normal University,Changchun 130024,China [3]Department of Statistics,University of Missouri,Columbia,MO 65211,USA

出　　处：《Journal of Systems Science & Complexity》2023年第3期1250-1264,共15页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos. 12001093,12071176;the National Key Research and Development Program of China under Grant No. 2020YFA0714102;the Science and Technology Developing Plan of Jilin Province under Grant No. 20200201258JC

摘　　要：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.

关 键 词：Current status data EM algorithm informative censoring MISCLASSIFICATION proportional hazard model 

分 类 号：O212.1[理学—概率论与数理统计]