Bootstrap methods for simultaneous benchmark analysis with quantal response data |
| |
Authors: | R Webster West Daniela K Nitcheva Walter W Piegorsch |
| |
Institution: | (1) Department of Statistics, Texas A&M University, College Station, TX 77843, USA;(2) Division of Biostatistics, South Carolina Department of Health and Environmental Control, Columbia, SC 29201, USA;(3) Department of Mathematics and BIO5 Institute, University of Arizona, Tucson, AZ 85721, USA |
| |
Abstract: | A primary objective in quantitative risk assessment is the characterization of risk which is defined to be the likelihood
of an adverse effect caused by an environmental toxin or chemcial agent. In modern risk-benchmark analysis, attention centers
on the “benchmark dose” at which a fixed benchmark level of risk is achieved, with a lower confidence limits on this dose
being of primary interest. In practice, a range of benchmark risks may be under study, so that the individual lower confidence
limits on benchmark dose must be corrected for simultaneity in order to maintain a specified overall level of confidence.
For the case of quantal data, simultaneous methods have been constructed that appeal to the large sample normality of parameter
estimates. The suitability of these methods for use with small sample sizes will be considered. A new bootstrap technique
is proposed as an alternative to the large sample methodology. This technique is evaluated via a simulation study and examples
from environmental toxicology.
|
| |
Keywords: | Benchmark dose Bootstrap Multistage model Quantal data Quantitative risk assessment Simultaneous inferences |
本文献已被 SpringerLink 等数据库收录! |
|