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Activity Number:
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20
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #308521 |
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Title:
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Analysis of Immunohistochemical Data Using Measurement Error Models
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Author(s):
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Ronglai Shen*+ and Debashis Ghosh and Jeremy Taylor
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Companies:
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University of Michigan and University of Michigan and University of Michigan
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Address:
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1789 Beal Ave Apt 2, Ann Arbor, MI, 48105,
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Keywords:
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Biomarker ; Immunohistochemical data ; Latent Expression Index ; Measurement error ; Tissue Microarray
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Abstract:
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The advent of Tissue Microarrays (TMAs) has provided a proteomic platform for validating cancer biomarkers emerging from large-scale DNA microarray studies. Repeated observations from each tumor result in substantial biological and experimental variability. We propose to analyze TMA data with patient survival endpoints in a measurement error model framework. Two goals are explored: 1) in a two-stage approach, a Latent Expression Index (LEI) is introduced as a summary protein expression index estimated from the TMA measurements; 2) a joint model of survival and TMA expression data is established via a shared random effect. Bayesian estimation is carried out using a Markov Chain Monte Carlo (MCMC) method. In a case study, we compared the expression estimates from the proposed error methods in differentiating risks of developing PSA failure among surgically treated prostate cancer patients.
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