Regression analysis of clustered current status data with informative cluster size under a transformed survival model

Feng Yanqin, Yin Shijiao and Ding Jieli ()
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Feng Yanqin: School of Mathematics and Statistics, 12390 Wuhan University , Wuhan, 430072, P.R. China
Yin Shijiao: School of Mathematics and Statistics, 12390 Wuhan University , Wuhan, 430072, P.R. China
Ding Jieli: School of Mathematics and Statistics, 12390 Wuhan University , Wuhan, 430072, P.R. China

The International Journal of Biostatistics, 2025, vol. 21, issue 1, 97-113

Abstract: In this paper, we study inference methods for regression analysis of clustered current status data with informative cluster sizes. When the correlated failure times of interest arise from a general class of semiparametric transformation frailty models, we develop a nonparametric maximum likelihood estimation based method for regression analysis and conduct an expectation-maximization algorithm to implement it. The asymptotic properties including consistency and asymptotic normality of the proposed estimators are established. Extensive simulation studies are conducted and indicate that the proposed method works well. The developed approach is applied to analyze a real-life data set from a tumorigenicity study.

Keywords: clustered current status data; semiparametric transformation frailty models; nonparametric maximum likelihood; expectation-maximization algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/ijb-2023-0130

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