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A Mixture Model for Bivariate Interval-Censored Failure Times with Dependent Susceptibility

Author

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  • Shu Jiang

    (University of Waterloo)

  • Richard J. Cook

    (University of Waterloo)

Abstract

Interval-censored failure times arise when the status with respect to an event of interest is only determined at intermittent examination times. In settings where there exists a sub-population of individuals who are not susceptible to the event of interest, latent variable models accommodating a mixture of susceptible and nonsusceptible individuals are useful. We consider such models for the analysis of bivariate interval-censored failure time data with a model for bivariate binary susceptibility indicators and a copula model for correlated failure times given joint susceptibility. We develop likelihood, composite likelihood, and estimating function methods for model fitting and inference, and assess asymptotic-relative efficiency and finite sample performance. Extensions dealing with higher-dimensional responses and current status data are also described.

Suggested Citation

  • Shu Jiang & Richard J. Cook, 2020. "A Mixture Model for Bivariate Interval-Censored Failure Times with Dependent Susceptibility," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 37-62, April.
    • Handle: RePEc:spr:stabio:v:12:y:2020:i:1:d:10.1007_s12561-020-09270-7
    DOI: 10.1007/s12561-020-09270-7
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    References listed on IDEAS

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    1. Nilanjan Chatterjee & Joanna Shih, 2001. "A Bivariate Cure-Mixture Approach for Modeling Familial Association in Diseases," Biometrics, The International Biometric Society, vol. 57(3), pages 779-786, September.
    2. Tao Sun & Yi Liu & Richard J. Cook & Wei Chen & Ying Ding, 2019. "Copula-based score test for bivariate time-to-event data, with application to a genetic study of AMD progression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 546-568, July.
    3. Yingwei Peng & Keith B. G. Dear, 2000. "A Nonparametric Mixture Model for Cure Rate Estimation," Biometrics, The International Biometric Society, vol. 56(1), pages 237-243, March.
    4. Liuquan Sun & Lianming Wang & Jianguo Sun, 2006. "Estimation of the Association for Bivariate Interval‐censored Failure Time Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(4), pages 637-649, December.
    5. Judy P. Sy & Jeremy M. G. Taylor, 2000. "Estimation in a Cox Proportional Hazards Cure Model," Biometrics, The International Biometric Society, vol. 56(1), pages 227-236, March.
    6. K. F. Lam & Hongqi Xue, 2005. "A semiparametric regression cure model with current status data," Biometrika, Biometrika Trust, vol. 92(3), pages 573-586, September.
    7. Peng, Yingwei, 2003. "Fitting semiparametric cure models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 481-490, January.
    8. W. J. Braun & J. E. Stafford, 2016. "Multivariate density estimation for interval‐censored data with application to a forest fire modelling problem," Environmetrics, John Wiley & Sons, Ltd., vol. 27(6), pages 345-354, September.
    9. Rebecca A. Betensky & Jane C. Lindsey & Louise M. Ryan & M. P. Wand, 1999. "Local EM Estimation of the Hazard Function for Interval-Censored Data," Biometrics, The International Biometric Society, vol. 55(1), pages 238-245, March.
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    Cited by:

    1. Fangya Mao & Richard J. Cook, 2023. "Spatial dependence modeling of latent susceptibility and time to joint damage in psoriatic arthritis," Biometrics, The International Biometric Society, vol. 79(3), pages 2605-2618, September.

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