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A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome

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  • Feng Gao
  • J. Miller
  • Chengjie Xiong
  • Julia Beiser
  • Mae Gordon

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Suggested Citation

  • Feng Gao & J. Miller & Chengjie Xiong & Julia Beiser & Mae Gordon, 2011. "A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 83-100, March.
    • Handle: RePEc:spr:stmapp:v:20:y:2011:i:1:p:83-100
    DOI: 10.1007/s10260-010-0150-z
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    References listed on IDEAS

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    1. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    2. Guo X. & Carlin B.P., 2004. "Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages," The American Statistician, American Statistical Association, vol. 58, pages 16-24, February.
    3. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    4. Jane Xu & Scott L. Zeger, 2001. "The Evaluation of Multiple Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 57(1), pages 81-87, March.
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    Cited by:

    1. Rui Martins, 2022. "A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 41-61, March.

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