Regression analysis of current status data with latent variables

Chunjie Wang (), Bo Zhao (), Linlin Luo () and Xinyuan Song ()
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Chunjie Wang: Changchun University of Technology
Bo Zhao: Changchun University of Technology
Linlin Luo: Changchun University of Technology
Xinyuan Song: The Chinese University of Hong Kong

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2021, vol. 27, issue 3, No 4, 413-436

Abstract: Abstract Current status data occur in many fields including demographical, epidemiological, financial, medical, and sociological studies. We consider the regression analysis of current status data with latent variables. The proposed model consists of a factor analytic model for characterizing latent variables through their multiple surrogates and an additive hazard model for examining potential covariate effects on the hazards of interest in the presence of current status data. We develop a borrow-strength estimation procedure that incorporates the expectation–maximization algorithm and correlated estimating equations. The consistency and asymptotic normality of the proposed estimators are established. A simulation study is conducted to evaluate the finite sample performance of the proposed method. A real-life study on the chronic kidney disease of type 2 diabetic patients is presented.

Keywords: Additive hazard model; Corrected estimating equations; Current status data; Factor analysis; Latent variables (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10985-021-09521-9

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