Bayesian and Frequentist Approaches for the Analysis of Multiple Endpoints Data Resulting from Exposure to Multiple Health Stressors.

• Main Author: Nyirabahizi, Epiphanie
• Format: Others
• Published: VCU Scholars Compass 2010
• Subjects: Benchmark dose tolerable area; Multiple endpoints data; Benchmark dose tolerable region; Bayesian Hierarchical Models; MCMC; Bayesian Model averaging; Composite scores.; Biostatistics; Physical Sciences and Mathematics; Statistics and Probability
• Online Access: http://scholarscompass.vcu.edu/etd/136; http://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1135&context=etd

In risk analysis, Benchmark dose (BMD)methodology is used to quantify the risk associated with exposure to stressors such as environmental chemicals. It consists of fitting a mathematical model to the exposure data and the BMD is the dose expected to result in a pre-specified response or benchmark response (BMR). Most available exposure data are from single chemical exposure, but living objects are exposed to multiple sources of hazards. Furthermore, in some studies, researchers may observe multiple endpoints on one subject. Statistical approaches to address multiple endpoints problem can be partitioned into a dimension reduction group and a dimension preservative group. Composite scores using desirability function is used, as a dimension reduction method, to evaluate neurotoxicity effects of a mixture of five organophosphate pesticides (OP) at a fixed mixing ratio ray, and five endpoints were observed. Then, a Bayesian hierarchical model approach, as a single unifying dimension preservative method is introduced to evaluate the risk associated with the exposure to mixtures chemicals. At a pre-specied vector of BMR of interest, the method estimates a tolerable area referred to as benchmark dose tolerable area (BMDTA) in multidimensional Euclidean plan. Endpoints defining the BMDTA are determined and model uncertainty and model selection problems are addressed by using the Bayesian Model Averaging (BMA) method.