@article{Chiu_Wright_Rusyn_2017, title={A tiered, Bayesian approach to estimating population variability for regulatory decision-making}, volume={34}, url={https://altex.org/index.php/altex/article/view/34}, DOI={10.14573/altex.1608251}, abstractNote={<p>Characterizing human variability in susceptibility to chemical toxicity is a critical issue in regulatory decision-making, but is usually addressed by a default 10-fold safety/uncertainty factor. Feasibility of population-based in vitro experimental approaches to more accurately estimate human variability was demonstrated recently using a large (~1000) panel of lymphoblastoid cell lines. However, routine use of such a large population-based model poses cost and logistical challenges. We hypothesize that a Bayesian approach embedded in a tiered workflow provides efficient estimation of variability and enables a tailored and sensible approach to selection of appropriate sample size. We used the previously collected lymphoblastoid cell line in vitro toxicity data to develop a data-derived prior distribution for the uncertainty in the degree of population variability. The resulting prior for the toxicodynamic variability factor (the ratio between the median and 1% most sensitive individuals) has a median (90% CI) of 2.5 (1.4-9.6). We then performed computational experiments using a hierarchical Bayesian population model with lognormal population variability with samples sizes of n = 5 to 100 to determine the change in precision and accuracy with increasing sample size. We propose a tiered Bayesian strategy for fit-for-purpose population variability estimates: (1) a default using the data-derived prior distribution; (2) a pilot experiment using samples sizes of ~20 individuals that reduces prior uncertainty by &gt; 50% with &gt; 80% balanced accuracy for classification; and (3) a high confidence experiment using sample sizes of ~50-100. This approach efficiently uses in vitro data on population variability to inform decision-making.</p>}, number={3}, journal={ALTEX - Alternatives to animal experimentation}, author={Chiu, Weihsueh A. and Wright, Fred A. and Rusyn, Ivan}, year={2017}, month={Aug.}, pages={377–388} }