Title: An open-sourced statistical application for identifying complex toxicological interactions of environmental pollutants.

Authors: Perkins, Jordan T; Petriello, Michael C; Xu, Li; Stromberg, Arnold; Hennig, Bernhard

Published In Rev Environ Health, (2017 Mar 01)

Abstract: The rising number of chemicals that humans are exposed to on a daily basis, as well as advances in biomonitoring and detection technologies have highlighted the diversity of individual exposure profiles (complex body burdens). To address this, the toxicological sciences have begun to shift away from examining toxic agents or stressors individually to focusing on more complex models with multiple agents or stressors present. Literature on interactions between chemicals is fairly limited in comparison with dose-response studies on individual toxicants, which is largely due to experimental and statistical challenges. Experimental designs capable of identifying these complex interactions are often avoided or not evaluated to their fullest potential because of the difficulty associated with appropriate analysis as well as logistical factors. To assist with statistical analysis of these types of experiments, an online, open-sourced statistical application was created for investigators to use to analyze and interpret potential toxicant interactions in laboratory experimental data using a full-factorial three-way analysis of variance (ANOVA). This model utilizes backward selection on interaction terms to model main effects and interactions.

PubMed ID: 28118146

MeSH Terms: Data Interpretation, Statistical; Environmental Monitoring/methods*; Environmental Pollutants/analysis; Environmental Pollutants/toxicity*; Hazardous Substances/analysis; Hazardous Substances/toxicity*; Humans; Models, Theoretical