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“Smooth” Semiparametric Regression Analysis for Arbitrarily Censored Time-to-Event Data

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  • Min Zhang
  • Marie Davidian

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  • Min Zhang & Marie Davidian, 2008. "“Smooth” Semiparametric Regression Analysis for Arbitrarily Censored Time-to-Event Data," Biometrics, The International Biometric Society, vol. 64(2), pages 567-576, June.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:2:p:567-576
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00928.x
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    References listed on IDEAS

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    1. Tianxi Cai & Rebecca A. Betensky, 2003. "Hazard Regression for Interval-Censored Data with Penalized Spline," Biometrics, The International Biometric Society, vol. 59(3), pages 570-579, September.
    2. Robert J. Gray, 2000. "Estimation of Regression Parameters and the Hazard Function in Transformed Linear Survival Models," Biometrics, The International Biometric Society, vol. 56(2), pages 571-576, June.
    3. Zhezhen Jin, 2003. "Rank-based inference for the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(2), pages 341-353, June.
    4. Wei Pan & Rick Chappell, 2002. "Estimation in the Cox Proportional Hazards Model with Left-Truncated and Interval-Censored Data," Biometrics, The International Biometric Society, vol. 58(1), pages 64-70, March.
    5. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    6. Jiang Lin & Daowen Zhang & Marie Davidian, 2006. "Smoothing Spline-Based Score Tests for Proportional Hazards Models," Biometrics, The International Biometric Society, vol. 62(3), pages 803-812, September.
    7. Fenton, Victor M. & Gallant, A. Ronald, 1996. "Qualitative and asymptotic performance of SNP density estimators," Journal of Econometrics, Elsevier, vol. 74(1), pages 77-118, September.
    8. Els Goetghebeur & Louise Ryan, 2000. "Semiparametric Regression Analysis of Interval-Censored Data," Biometrics, The International Biometric Society, vol. 56(4), pages 1139-1144, December.
    9. Daowen Zhang & Marie Davidian, 2001. "Linear Mixed Models with Flexible Distributions of Random Effects for Longitudinal Data," Biometrics, The International Biometric Society, vol. 57(3), pages 795-802, September.
    10. Lu Tian & Tianxi Cai, 2006. "On the accelerated failure time model for current status and interval censored data," Biometrika, Biometrika Trust, vol. 93(2), pages 329-342, June.
    11. Chris T. Volinsky & Adrian E. Raftery, 2000. "Bayesian Information Criterion for Censored Survival Models," Biometrics, The International Biometric Society, vol. 56(1), pages 256-262, March.
    12. Zhezhen Jin & D. Y. Lin & Zhiliang Ying, 2006. "On least-squares regression with censored data," Biometrika, Biometrika Trust, vol. 93(1), pages 147-161, March.
    13. Wei Pan, 2000. "A Multiple Imputation Approach to Cox Regression with Interval-Censored Data," Biometrics, The International Biometric Society, vol. 56(1), pages 199-203, March.
    14. Kani Chen, 2002. "Semiparametric analysis of transformation models with censored data," Biometrika, Biometrika Trust, vol. 89(3), pages 659-668, August.
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    Cited by:

    1. Scolas, Sylvie & El Ghouch, Anouar & Legrand, Catherine, 2016. "The SNP representation in mixture cure models with interval-censoring: estimation and goodness-of-fit testing," LIDAM Discussion Papers ISBA 2016049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Vock, David & Davidian, Marie & Tsiatis, Anastasios, 2014. "SNP_NLMM: A SAS Macro to Implement a Flexible Random Effects Density for Generalized Linear and Nonlinear Mixed Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(c02).
    3. Prabhashi W. Withana Gamage & Christopher S. McMahan & Lianming Wang, 2023. "A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 188-212, January.

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