title: Bayesian isotonic regression dose-response model.
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 Emaxmodel and the logistic model. These parametric models are easy to interpret and, hence, are 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 robust estimation, nonparametric models are used. However, these models are not able to estimate certain important clinical parameters, such as ED50and Emax. Furthermore, in many therapeutic areas, dose-response curves can be assumed as nondecreasing functions. This creates an additional challenge for nonparametric methods. In this paper, we propose a new Bayesian isotonic regression dose-response (BIRD) which features advantages from both parametric and nonparametric models. The ED50and Emaxcan be derived from this model. Simulations are provided to evaluate the BIRD model performance against two parametric models. We apply this model to a dataset from a diabetes dose-finding study.
authors.affiliation: a Merck & Co., Inc ., Kenilworth , New Jersey , USA.
authors.forename: Wen
authors.initials: W
authors.lastname: Li
authors.affiliation: b Eli Lilly and Company , Indianapolis , Indiana , USA.
authors.forename: Haoda
authors.initials: H
authors.lastname: Fu
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation / Q000706:statistics & numerical data*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016015:Logistic Models; D004364:Pharmaceutical Preparations / Q000008:administration & dosage*
chemical_list: D004364:Pharmaceutical Preparations
keywords: Bayesian method; dose response; isotonic regression; monotone function estimation