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Regression analysis of current status data with latent variables

Author

Listed:
  • Chunjie Wang

    (Changchun University of Technology)

  • Bo Zhao

    (Changchun University of Technology
    Heilongjiang Bayi Agricultural University)

  • Linlin Luo

    (Changchun University of Technology)

  • Xinyuan Song

    (The Chinese University of Hong Kong)

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.

Suggested Citation

  • Chunjie Wang & Bo Zhao & Linlin Luo & Xinyuan Song, 2021. "Regression analysis of current status data with latent variables," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(3), pages 413-436, July.
    • Handle: RePEc:spr:lifeda:v:27:y:2021:i:3:d:10.1007_s10985-021-09521-9
    DOI: 10.1007/s10985-021-09521-9
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    References listed on IDEAS

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    1. J. Sun, 1999. "A nonparametric test for current status data with unequal censoring," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 243-250.
    2. Sik-Yum Lee & Xin-Yuan Song, 2004. "Maximum Likelihood Analysis of a General Latent Variable Model with Hierarchically Mixed Data," Biometrics, The International Biometric Society, vol. 60(3), pages 624-636, September.
    3. Ling Ma & Tao Hu & Jianguo Sun, 2015. "Sieve maximum likelihood regression analysis of dependent current status data," Biometrika, Biometrika Trust, vol. 102(3), pages 731-738.
    4. Guoqing Diao & Ao Yuan, 2019. "A class of semiparametric cure models with current status data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 26-51, January.
    5. J.‐Q. Shi & S.‐Y. Lee, 2000. "Latent variable models with mixed continuous and polytomous data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 77-87.
    6. Deng Pan & Haijin He & Xinyuan Song & Liuquan Sun, 2015. "Regression Analysis of Additive Hazards Model With Latent Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1148-1159, September.
    7. Amemiya, Yasuo & Fuller, Wayne A. & Pantula, Sastry G., 1987. "The asymptotic distributions of some estimators for a factor analysis model," Journal of Multivariate Analysis, Elsevier, vol. 22(1), pages 51-64, June.
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