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Tuesday, January 7
Tue, Jan 7, 7:45 AM - 8:45 AM
Pacific D
Continental Breakfast & Poster Session II

Monotonic Nonparametric Dose Response Model (307856)

*Faten Alamri, Princess Nourah Bint Abdul Rahman University & Virginia Commonwealth University 
Edward Boone, Virginia Commonwealth University 
David Edwards, Virginia Commonwealth University 

Keywords: Bayesian Statistics; Nonparametric modeling; Alamri Monotonic spline; Toxicology data; Benchmark tolerable region

Toxicologists are often concerned with determining the dosage at which an individual can be exposed to with an acceptable risk of adverse effect. These types of studies have been conducted widely in the past and many novel approaches have been developed. Parametric techniques utilizing ANOVA and non-linear regression models are well represented in the literature. The biggest drawback of parametric approaches is the need to specify the correct/best model. Recently, there has been an interest in non-parametric approaches to tolerable dosage estimation. In this work, we focus on the situation where the response is a percent to control which is known to be monotonically decreasing. This poses two constraints to the non-parametric approach where the dose-response function must be one at control (dose=0) and the function must always be positive. Here we propose a Bayesian solution to this problem using a novel class of non-parametric models using a new set of basis functions. Alamri Monotonic spline (AM-spline) function developed in this research. This is illustrated using both simulated data and a dataset from the US Environmental Protection Agency concerning exposure to pesticides.