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A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer

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  • Rebecca Yates Coley
  • Aaron J. Fisher
  • Mufaddal Mamawala
  • Herbert Ballentine Carter
  • Kenneth J. Pienta
  • Scott L. Zeger

Abstract

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Suggested Citation

  • Rebecca Yates Coley & Aaron J. Fisher & Mufaddal Mamawala & Herbert Ballentine Carter & Kenneth J. Pienta & Scott L. Zeger, 2017. "A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer," Biometrics, The International Biometric Society, vol. 73(2), pages 625-634, June.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:2:p:625-634
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    File URL: http://hdl.handle.net/10.1111/biom.12577
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    References listed on IDEAS

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    1. Margaret Pepe & Holly Janes & Gary Longton & Wendy Leisenring & Polly Newcomb, 2004. "Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic or Prognostic Marker," UW Biostatistics Working Paper Series 1035, Berkeley Electronic Press.
    2. Erika Check Hayden, 2009. "Personalized cancer therapy gets closer," Nature, Nature, vol. 458(7235), pages 131-132, March.
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    Cited by:

    1. Li, Weiyu & Denton, Brian T. & Morgan, Todd M., 2023. "Optimizing active surveillance for prostate cancer using partially observable Markov decision processes," European Journal of Operational Research, Elsevier, vol. 305(1), pages 386-399.

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