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Abstract Details
Activity Number:
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326
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #305653 |
Title:
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Ornstein-Uhlenbeck Random Effects Threshold Regression for Survival Data from Heterogeneous Populations
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Author(s):
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Roger Erich and Michael Lindsey Pennell*+
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Companies:
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The Ohio State University and The Ohio State University College of Public Health
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Address:
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246 Cunz Hall , Columbus, OH, , USA
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Keywords:
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Cancer clinical trial ;
Cure rate ;
First hitting time model ;
Gaussian process ;
Mixture model ;
Random effects
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Abstract:
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Threshold regression (TR) is a relatively new methodology which models time to event data using a latent stochastic process which fails once it hits a boundary value. For instance, in a biomedical study, the latent process would correspond to patient health with disease or death occurring once the process has reached a threshold. The Ornstein-Uhlenbeck (OU) process is an attractive choice for these applications because its increments are dependent on the current state of the process and it operates around a point of homeostasis. Current methods assume that the expected path of the process is identical for subjects with the same covariate values. This assumption may not be plausible for many human populations as one would expect a great deal of heterogeneity in their unmeasured covariates (e.g., genetic factors) which likely affect the different parameters of the OU process. In this presentation, we will address this problem by extending previous OU process TR models to include a random subject-specific effect in the initial state of the process. We will also present an extension of our model to include a cure rate and we will apply our methods to a cancer clinical trial.
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