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Semiparametric Likelihood Estimation in the Clayton–Oakes Failure Time Model

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  • D. V. Glidden
  • S. G. Self

Abstract

Multivariate failure time data arise when the sample consists of clusters and each cluster contains several possibly dependent failure times. The Clayton–Oakes model (Clayton, 1978; Oakes, 1982) for multivariate failure times characterizes the intracluster dependence parametrically but allows arbitrary specification of the marginal distributions. In this paper, we discuss estimation in the Clayton–Oakes model when the marginal distributions are modeled to follow the Cox (1972) proportional hazards regression model. Parameter estimation is based on an approximate generalized maximum likelihood estimator. We illustrate the model's application with example datasets.

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  • D. V. Glidden & S. G. Self, 1999. "Semiparametric Likelihood Estimation in the Clayton–Oakes Failure Time Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(3), pages 363-372, September.
    • Handle: RePEc:bla:scjsta:v:26:y:1999:i:3:p:363-372
    DOI: 10.1111/1467-9469.00154
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    Cited by:

    1. Wenqing He & Jerald F. Lawless, 2003. "Flexible Maximum Likelihood Methods for Bivariate Proportional Hazards Models," Biometrics, The International Biometric Society, vol. 59(4), pages 837-848, December.
    2. Frank Eriksson & Torben Martinussen & Thomas H. Scheike, 2015. "Clustered Survival Data with Left-truncation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1149-1166, December.
    3. Ghosh Debashis, 2008. "On the Plackett Distribution with Bivariate Censored Data," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-22, May.
    4. L. Xue & L. Wang & A. Qu, 2010. "Incorporating Correlation for Multivariate Failure Time Data When Cluster Size Is Large," Biometrics, The International Biometric Society, vol. 66(2), pages 393-404, June.
    5. Dandan Liu & John D. Kalbfleisch & Douglas E. Schaubel, 2011. "A Positive Stable Frailty Model for Clustered Failure Time Data with Covariate-Dependent Frailty," Biometrics, The International Biometric Society, vol. 67(1), pages 8-17, March.
    6. Li Hsu & Lu Chen & Malka Gorfine & Kathleen Malone, 2004. "Semiparametric Estimation of Marginal Hazard Function from Case–Control Family Studies," Biometrics, The International Biometric Society, vol. 60(4), pages 936-944, December.
    7. Shuling Liu & Amita K. Manatunga & Limin Peng & Michele Marcus, 2017. "A joint modeling approach for multivariate survival data with random length," Biometrics, The International Biometric Society, vol. 73(2), pages 666-677, June.

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