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Activity Number:
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481
- Dose Finding, Dose Selection, and Early-Phase Trials
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Type:
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Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #322592
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Title:
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A Non-Parametric Bayesian Approach for Monotonic Dose-Response Model
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Author(s):
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Nairita Ghosal*
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Companies:
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Merck & Co., Inc.
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Keywords:
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Monotonic dose-response;
Bayesian non-parametric
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Abstract:
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Exploration of dose-response relationship plays an important and challenging role during drug development process. The main goal of dose finding studies are to determine a proof of concept to select a dose (or doses) for confirmatory Phase III studies. There exist many parametric techniques utilizing ANOVA and non-linear regression. However, the ability to apply parametric model may be limited to some extend due to small number of doses. In recent years, there have been some work applying semi-parametric and non-parametric Bayesian approaches to monotonic dose-response models. A non-parametric Bayesian model with connected piece-wise-linear dose-response function with prior distributions will be described in this presentation. Several examples will be produced to illustrate the method as an application.
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Authors who are presenting talks have a * after their name.
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