Regression Analysis of Dependent Current Status Data with Left Truncation

Mengyue Zhang, Shishun Zhao, Tao Hu, Da Xu () and Jianguo Sun
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Mengyue Zhang: Center for Applied Statistical Research, School of Mathematics, Jilin University, Changchun 130012, China
Shishun Zhao: Center for Applied Statistical Research, School of Mathematics, Jilin University, Changchun 130012, China
Tao Hu: School of Mathematical Sciences, Capital Normal University, Beijing 100048, China
Da Xu: Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China
Jianguo Sun: Department of Statistics, University of Missouri, Columbia, MO 65211, USA

Mathematics, 2023, vol. 11, issue 16, 1-13

Abstract: Current status data are encountered in a wide range of applications, including tumorigenic experiments and demographic studies. In this case, each subject has one observation, and the only information obtained is whether the event of interest happened at the moment of observation. In addition to censoring, truncating is also very common in practice. This paper examines the regression analysis of current status data with informative censoring times, considering the presence of left truncation. In addition, we propose an inference approach based on sieve maximum likelihood estimation (SMLE). A copula-based approach is used to describe the relationship between the failure time of interest and the censoring time. The spline function is employed to approximate the unknown nonparametric function. We have established the asymptotic properties of the proposed estimator. Simulation studies suggest that the developed procedure works well in practice. We also applied the developed method to a real dataset derived from an AIDS cohort research.

Keywords: current status data; left truncation; informative censoring; I-splines; copula model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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