A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer-Reference-Cited by-同舟云学术

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

Published:2016-08-22 Issue:2 Volume:73 Page:625-634
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biom

Author:

Coley Rebecca Yates¹,Fisher Aaron J.¹,Mamawala Mufaddal²,Carter Herbert Ballentine²³,Pienta Kenneth J.²³⁴,Zeger Scott L.¹

Affiliation:

1. Department of Biostatistics; Johns Hopkins University; Baltimore, Maryland 21205 U.S.A.

2. James Buchanan Brady Urological Institute; Johns Hopkins Medical Institutions; Baltimore, Maryland 21287 U.S.A.

3. Department of Oncology; Johns Hopkins Medical Institutions; Baltimore, Maryland 21287 U.S.A.

4. Department of Pharmacology; Johns Hopkins Medical Institutions; Baltimore, Maryland 21287

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/biom.12577/fullpdf

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3. Chapelle , O. Schölkopf , B. Zien , A. 2006 Semi-Supervised Learning

4. Chou , R. Dana , T. Bougatsos , C. Fu , R. Blazina , I. Gleitsmann , K. et al. 2011 Evidence Synthesis No. 91. ARHQ Publication No. 12-0516-EF-2. Rockville, MD: Agency for Healthcare Research and Quality

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