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Activity Number: 372
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #314784
Title: Bayesian Isotonic Regression Dose-Response
Author(s): Wen Li* and Jeffrey A. Davidson and Haoda Fu
Companies: Accenture Accelerated R&D Services and Accenture Accelerated R&D Services and Eli Lilly and Company
Keywords: Bayesian method ; dose response ; isotonic regression ; monotone function estimation
Abstract:

Understanding dose-response relationship is a crucial step in drug development. There are a few parametric methods to estimate dose-response curves, such as the Emax model and the logistic model. These parametric models are easy to interpret and, hence, widely used. However, these models often require the inclusion of patients on high-dose levels; otherwise, the model parameters cannot be reliably estimated. To have a robust estimation, nonparametric models are used. However, these models are not able to estimate certain important clinical parameters, such as ED50 and Emax. Furthermore, in many therapeutic areas, dose-response curves can be assumed as non-decreasing functions. This creates an additional challenge for nonparametric methods. In this paper, we propose a new Bayesian isotonic regression dose-response model which features advantages from both parametric and nonparametric models. The ED50 and Emax can be derived from this model. Simulations are provided to evaluate the Bayesian isotonic regression dose-response model performance against two parametric models. We apply this model to a data set from a diabetes dose-finding study.


Authors who are presenting talks have a * after their name.

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