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Abstract Details
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Activity Number:
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408
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 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 - #306797 |
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Title:
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Hierarchical Gaussian Process Latent Variable Model for Clinical Decisions
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Author(s):
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Yanxun Xu*+
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Companies:
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Rice University/MD Anderson Cancer Center
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Address:
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1400 Pressler Street, Houston, TX, 77030, United States
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Keywords:
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Gaussian Process ;
Hybrid Monte Carlo ;
Hierarchical latent variable model ;
Prediction
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Abstract:
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The Gaussian process latent variable model is a powerful approach and increasing of interest in statistics, engineering and other areas due to their good performance and desirable properties. In this paper, we introduce our model by firstly presenting a motivating clinical example, which has binary efficacy response. We develop a Bayesian interim analysis plan that allows investigators to make trial decisions, such as terminating the trial or to continue. Our method is constructed by utilizing the information from the patients we have observed, and a hybrid Markov chain Monte Carlo (MCMC) algorithm is used for its implementation. Through the posterior estimates of latent variables, we compute prediction at unknown time points. The simulation studies in several scenarios are reported, which can be easily extended to many other applications.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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