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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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第一作者： Coley, Rebecca Yates

作者： Coley, Rebecca Yates ^[1] ; Fisher, Aaron J. ^[2] ; Mamawala, Mufaddal ; Carter, Herbert Ballentine ; Pienta, Kenneth J. ; Zeger, Scott L.

作者单位： Johns Hopkins Med Inst, James Buchanan Brady Urol Inst, Baltimore, MD 21287 USA ^[1] Johns Hopkins Univ, Dept Biostat, Baltimore, MD 21205 USA ^[2]

关键词 Latent class analysis Missing data Precision medicine Prostate cancer prognosis Risk classification

发布时间 2019-09-05

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Biometrics

2017年73卷2期

625-634页

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2017年73卷2期

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

Biometrics

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Biometrics

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