Model Averaging with AIC Weights for Hypothesis Testing of Hormesis at Low Doses. Issue 2 (20th June 2017)

• Record Type: Journal Article
• Authors: Kim, Steven B.; Sanders, Nathan
• Is Part Of: Dose-response. Volume 15:Issue 2(2017:Apr./Jun.)
• Publication Date: 2017-06-20
• Subjects: hypothesis testing -- hormesis -- model misspecification -- model averaging -- Akaike information criterion; Dose-response relationship (Biochemistry) -- Periodicals; Drugs -- Dose-response relationship -- Periodicals; Drugs -- Physiological effect -- Periodicals; Hormesis -- Periodicals; Dose-Response Relationship, Drug -- Periodicals; Dose-response relationship (Biochemistry); Drugs -- Dose-response relationship; Drugs -- Physiological effect; Periodicals; 571.634
• DOI: 10.1177/1559325817715314
• Languages: English
• ISSNs: 1559-3258

Abstract

For many dose–response studies, large samples are not available. Particularly, when the outcome of interest is binary rather than continuous, a large sample size is required to provide evidence for hormesis at low doses. In a small or moderate sample, we can gain statistical power by the use of a parametric model. It is an efficient approach when it is correctly specified, but it can be misleading otherwise. This research is motivated by the fact that data points at high experimental doses have too much contribution in the hypothesis testing when a parametric model is misspecified. In dose–response analyses, to account for model uncertainty and to reduce the impact of model misspecification, averaging multiple models have been widely discussed in the literature. In this article, we propose to average semiparametric models when we test for hormesis at low doses. We show the different characteristics of averaging parametric models and averaging semiparametric models by simulation. We apply the proposed method to real data, and we show that P values from averaged semiparametric models are more credible than P values from averaged parametric methods. When the true dose–response relationship does not follow a parametric assumption, the proposed method can be an alternative robust approach.