caa a22 4500 605476276 CHVBK 20210128100355.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s10985-014-9303-y doi (NATIONALLICENCE)springer-10.1007/s10985-014-9303-y Regression analysis of informative current status data with the additive hazards model [Elektronische Daten] [Shishun Zhao, Tao Hu, Ling Ma, Peijie Wang, Jianguo Sun] This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided. Springer Science+Business Media New York, 2014 Additive hazards model nationallicence Current status data nationallicence Efficient estimation nationallicence Informative censoring nationallicence Zhao Shishun College of Mathematics, Jilin University, 130012, Changchun, People's Republic of China aut Hu Tao School of Mathematical Sciences and BCMIIS, Capital Normal University, 100048, Beijing, People's Republic of China aut Ma Ling Department of Statistics, University of Missouri, 146 Middlebush Hall, 65211, MO, USA aut Wang Peijie College of Mathematics, Jilin University, 130012, Changchun, People's Republic of China aut Sun Jianguo Department of Statistics, University of Missouri, 146 Middlebush Hall, 65211, MO, USA aut Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/2(2015-04-01), 241-258 1380-7870 21:2<241 2015 21 10985 https://doi.org/10.1007/s10985-014-9303-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10985-014-9303-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhao Shishun College of Mathematics, Jilin University, 130012, Changchun, People's Republic of China aut NATIONALLICENCE 700 1- Hu Tao School of Mathematical Sciences and BCMIIS, Capital Normal University, 100048, Beijing, People's Republic of China aut NATIONALLICENCE 700 1- Ma Ling Department of Statistics, University of Missouri, 146 Middlebush Hall, 65211, MO, USA aut NATIONALLICENCE 700 1- Wang Peijie College of Mathematics, Jilin University, 130012, Changchun, People's Republic of China aut NATIONALLICENCE 700 1- Sun Jianguo Department of Statistics, University of Missouri, 146 Middlebush Hall, 65211, MO, USA aut NATIONALLICENCE 773 0- Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/2(2015-04-01), 241-258 1380-7870 21:2<241 2015 21 10985