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Rejoinder for “Meta-analysis for Surrogacy: Accelerated Failure Time Models and Semicompeting Risks Modeling”

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  • Debashis Ghosh
  • Jeremy M. G. Taylor
  • Daniel J. Sargent

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  • Debashis Ghosh & Jeremy M. G. Taylor & Daniel J. Sargent, 2012. "Rejoinder for “Meta-analysis for Surrogacy: Accelerated Failure Time Models and Semicompeting Risks Modeling”," Biometrics, The International Biometric Society, vol. 68(1), pages 245-247, March.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:1:p:245-247
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01638.x
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    References listed on IDEAS

    as
    1. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    2. Debashis Ghosh, 2009. "On Assessing Surrogacy in a Single Trial Setting Using a Semicompeting Risks Paradigm," Biometrics, The International Biometric Society, vol. 65(2), pages 521-529, June.
    3. Junni L. Zhang & Donald B. Rubin, 2003. "Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Deathâ€," Journal of Educational and Behavioral Statistics, , vol. 28(4), pages 353-368, December.
    4. Jinfeng Xu & John D. Kalbfleisch & Beechoo Tai, 2010. "Statistical Analysis of Illness–Death Processes and Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 66(3), pages 716-725, September.
    5. Yun Li & Jeremy M.G. Taylor & Michael R. Elliott, 2010. "A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials," Biometrics, The International Biometric Society, vol. 66(2), pages 523-531, June.
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    Cited by:

    1. Kyu Ha Lee & Virginie Rondeau & Sebastien Haneuse, 2017. "Accelerated failure time models for semi‐competing risks data in the presence of complex censoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1401-1412, December.
    2. Fan, Caiyun & Lu, Wenbin & Zhou, Yong, 2021. "Testing error heterogeneity in censored linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).

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