Bayesian Quantile Impairment Threshold Benchmark Dose Estimation for Continuous Endpoints
Wheeler, MW; Bailer, AJ; Cole, T; Park, RM; Shao, K
HERO ID
4170410
Reference Type
Journal Article
Year
2017
Language
English
PMID
| HERO ID | 4170410 |
|---|---|
| In Press | No |
| Year | 2017 |
| Title | Bayesian Quantile Impairment Threshold Benchmark Dose Estimation for Continuous Endpoints |
| Authors | Wheeler, MW; Bailer, AJ; Cole, T; Park, RM; Shao, K |
| Journal | Risk Analysis |
| Volume | 37 |
| Issue | 11 |
| Page Numbers | 2107-2118 |
| Abstract | Quantitative risk assessment often begins with an estimate of the exposure or dose associated with a particular risk level from which exposure levels posing low risk to populations can be extrapolated. For continuous exposures, this value, the benchmark dose, is often defined by a specified increase (or decrease) from the median or mean response at no exposure. This method of calculating the benchmark dose does not take into account the response distribution and, consequently, cannot be interpreted based upon probability statements of the target population. We investigate quantile regression as an alternative to the use of the median or mean regression. By defining the dose-response quantile relationship and an impairment threshold, we specify a benchmark dose as the dose associated with a specified probability that the population will have a response equal to or more extreme than the specified impairment threshold. In addition, in an effort to minimize model uncertainty, we use Bayesian monotonic semiparametric regression to define the exposure-response quantile relationship, which gives the model flexibility to estimate the quantal dose-response function. We describe this methodology and apply it to both epidemiology and toxicology data. |
| Doi | 10.1111/risa.12762 |
| Pmid | 28555874 |
| Wosid | WOS:000414866800009 |
| Url | https://www.ncbi.nlm.nih.gov/pubmed/28555874 |
| Is Certified Translation | No |
| Dupe Override | No |
| Is Public | Yes |
| Language Text | English |
| Keyword | Animal toxicity studies; monotone smoothing splines; risk assessment; semiparametric modeling |