A parametric model for detecting hormetic effects in developmental toxicity studies.
Hormetic effects have been observed at low exposure levels based on the dose-response pattern of data from developmental toxicity studies. This indicates that there might actually be a reduced risk of exhibiting toxic effects at low exposure levels. Hormesis implies the existence of a threshold dose level and there are dose-response models that include parameters that account for the threshold. We propose a function that introduces a parameter to account for hormesis. This function is a subset of the set of all functions that could represent a hormetic dose-response relationship at low exposure levels to toxic agents. We characterize the overall dose-response relationship with a piecewise function that consists of a hormetic u-shape curve at low dose levels and a logistic curve at high dose levels. We apply our model to a data set from an experiment conducted at the National Toxicology Program (NTP). We also use the beta-binomial distribution to model the litter response data. It can be seen by observing the structure of these data that current experimental designs for developmental studies employ a limited number of dose groups. These designs may not be satisfactory when the goal is to illustrate the existence of hormesis. In particular, increasing the number of low-level doses improves the power for detecting hormetic effects. Therefore, we also provide the results of simulations that were done to characterize the power of current designs in detecting hormesis and to demonstrate how this power can be improved upon by altering these designs with the addition of only a few low exposure levels.
Hunt
Daniel L
DL
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
Bowman
Dale
D
D000818:Animals; D003198:Computer Simulation; D015509:Developmental Biology / Q000706:statistics & numerical data; D004305:Dose-Response Relationship, Drug; D005314:Embryonic and Fetal Development / Q000187:drug effects; D005260:Female; D016013:Likelihood Functions; D008954:Models, Biological; D015233:Models, Statistical; D011247:Pregnancy; D018570:Risk Assessment; D018675:Toxicity Tests / Q000706:statistics & numerical data*