A tiered, Bayesian approach to estimating of population variability for regulatory decision-making
Chiu, WA; Wright, FA; Rusyn, I
HERO ID
4215953
Reference Type
Journal Article
Year
2017
Language
English
PMID
| HERO ID | 4215953 |
|---|---|
| In Press | No |
| Year | 2017 |
| Title | A tiered, Bayesian approach to estimating of population variability for regulatory decision-making |
| Authors | Chiu, WA; Wright, FA; Rusyn, I |
| Journal | ALTEX |
| Volume | 34 |
| Issue | 3 |
| Page Numbers | 377-388 |
| Abstract | 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 > 50% with > 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. |
| Doi | 10.14573/altex.1608251 |
| Pmid | 27960008 |
| Wosid | WOS:000406051600005 |
| Is Certified Translation | No |
| Dupe Override | No |
| Is Public | Yes |
| Language Text | English |
| Keyword | variability; Bayesian; in vitro; uncertainty |