| Literature DB >> 26873961 |
Steven B Kim1, Scott M Bartell2, Daniel L Gillen2.
Abstract
In toxicology studies hormesis refers to a dose-response relationship with a stimulatory response at low doses and an inhibitory response at high doses. In this manuscript, we particularly focus on a J-shaped dose-response relationship for binary cancer responses. We propose and examine two new flexible models for testing the hypothesis of hormesis in a Bayesian framework. The first model is parametric and enhances the flexibility of modeling a hormetic zone by using a non-linear predictor in a multistage model. The second model is non-parametric and allows multiple model specifications, weighting the contribution of each model via Bayesian model averaging (BMA). Simulation studies show that the non-parametric modeling approach with BMA provides robust sensitivity and specificity for detecting hormesis relative to the parametric approach, regardless of the shape of a hormetic zone.Entities:
Keywords: Bayesian model averaging; Hormesis; Hypothesis testing; Multistage models; Non-parametric models
Mesh:
Year: 2016 PMID: 26873961 PMCID: PMC4915611 DOI: 10.1093/biostatistics/kxw004
Source DB: PubMed Journal: Biostatistics ISSN: 1465-4644 Impact factor: 5.899