Abstract Details
Activity Number:
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271
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Type:
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Contributed
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Date/Time:
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Monday, August 10, 2015 : 3:05 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #317850
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Title:
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A Simulation Study for Bivariate Wiener Process Models for an Observable Marker and the Latent Health Status
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Author(s):
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Sara Conroy* and Michael Pennell
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Companies:
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and The Ohio State University
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Keywords:
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Threshold regression ;
bivariate Wiener process ;
First-hitting time model ;
Non-proportional hazards
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Abstract:
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Threshold regression is a relatively new model for time to event data in which the health status of a subject is modeled using an unobservable stochastic process which reaches a threshold when the subject fails. In addition to being a conceptually appealing model, it does not require the proportional hazards assumption of the Cox model. A few authors have proposed bivariate Wiener process models for an observable marker and the latent health status. We ran a simulation study to look at the benefits of the joint modeling in the context of a clinical trial. Specifically, we looked at the effects of joint modeling on effect of treatment on time to event at different sample sizes and different correlation levels between the health status and marker processes. Bias was small and did not show a pattern, but the standard error of the treatment effect estimate decreased as sample size and the correlation increased.
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Authors who are presenting talks have a * after their name.
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