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Semiparametric time-to-event modeling in the presence of a latent progression event

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  • John D. Rice
  • Alex Tsodikov

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  • John D. Rice & Alex Tsodikov, 2017. "Semiparametric time-to-event modeling in the presence of a latent progression event," Biometrics, The International Biometric Society, vol. 73(2), pages 463-472, June.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:2:p:463-472
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    File URL: http://hdl.handle.net/10.1111/biom.12580
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    References listed on IDEAS

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    1. Yi-Hau Chen, 2009. "Weighted Breslow-type and maximum likelihood estimation in semiparametric transformation models," Biometrika, Biometrika Trust, vol. 96(3), pages 591-600.
    2. A. Tsodikov, 2003. "Semiparametric models: a generalized self‐consistency approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(3), pages 759-774, August.
    3. Maya R. Sternberg & Glen A. Satten, 1999. "Discrete-Time Nonparametric Estimation for Semi-Markov Models of Chain-of-Events Data Subject to Interval Censoring and Truncation," Biometrics, The International Biometric Society, vol. 55(2), pages 514-522, June.
    4. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    5. Halina Frydman & Michael Szarek, 2009. "Nonparametric Estimation in a Markov “Illness–Death” Process from Interval Censored Observations with Missing Intermediate Transition Status," Biometrics, The International Biometric Society, vol. 65(1), pages 143-151, March.
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    Cited by:

    1. Theresa P. Devasia & Alexander Tsodikov, 2024. "Efficiency of the Breslow estimator in semiparametric transformation models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(2), pages 291-309, April.

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