A New Stochastic Kriging Method for Modeling Multi-Source Exposure-Response Data in Toxicology Studies

One of the most fundamental steps in risk assessment is to quantify the exposure-response relationship for the material/chemical of interest. This work develops a new statistical method, referred to as SKQ (stochastic kriging with qualitative factors), to synergistically model exposure-response data, which often arise from multiple sources (e.g., laboratories, animal providers, and shapes of nanomaterials) in toxicology studies. Compared to the existing methods, SKQ has several distinct features. First, SKQ integrates data across multiple sources and allows for the derivation of more accurate information from limited data. Second, SKQ is highly flexible and able to model practically any continuous response surfaces (e.g., dose-time-response surface). Third, SKQ is able to accommodate variance heterogeneity across experimental conditions and to provide valid statistical inference (i.e., quantify uncertainties of the model estimates). Through empirical studies, we have demonstrated SKQ's ability to efficiently model exposure-response surfaces by pooling information across multiple data sources. SKQ fits into the mosaic of efficient decision-making methods for assessing the risk of a tremendously large variety of nanomaterials and helps to alleviate safety concerns regarding the enormous amount of new nanomaterials.