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A new joint model for longitudinal and survival data with a cure fraction

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  • Chen, Ming-Hui
  • Ibrahim, Joseph G.
  • Sinha, Debajyoti
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    Abstract

    We develop a new joint cure rate model for longitudinal and survival data. The model allows for multiple longitudinal markers as well as a cure structure for the survival component based on the promotion time cure rate model, as described in Ibrahim et al. (Bayesian Survival Analysis, Springer, New York, 2001). Several characteristics and properties of the new model are discussed and examined. A real dataset from a melanoma clinical trial is given to demonstrate the methodology.

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    File URL: http://www.sciencedirect.com/science/article/B6WK9-4CHRY4B-1/2/112862d204d0b90bf562e04bcd5fdbd1
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 91 (2004)
    Issue (Month): 1 (October)
    Pages: 18-34

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    Handle: RePEc:eee:jmvana:v:91:y:2004:i:1:p:18-34

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    Related research

    Keywords: Antibody IgG titers Antibody IgM titers Biologic markers Cancer Longitudinal data Melanoma Cure rate model Random effects Survival model;

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    Cited by:
    1. Shuangge Ma, 2011. "Additive risk model for current status data with a cured subgroup," Annals of the Institute of Statistical Mathematics, Springer, vol. 63(1), pages 117-134, February.
    2. Sik-Yum Lee & Ye-Mao Xia, 2008. "A Robust Bayesian Approach for Structural Equation Models with Missing Data," Psychometrika, Springer, vol. 73(3), pages 343-364, September.

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