Estimating Upper Confidence Limits for Extra Risk in Quantal Multistage Models

A. John Bailer and Randall J. Smith

Risk Analysis, 1994, vol. 14, issue 6, 1001-1010

Abstract: Multistage models are frequently applied in carcinogenic risk assessment. In their simplest form, these models relate the probability of tumor presence to some measure of dose. These models are then used to project the excess risk of tumor occurrence at doses frequently well below the lowest experimental dose. Upper confidence limits on the excess risk associated with exposures at these doses are then determined. A likelihood‐based method is commonly used to determine these limits. We compare this method to two computationally intensive “bootstrap” methods for determining the 95% upper confidence limit on extra risk. The coverage probabilities and bias of likelihood‐based and bootstrap estimates are examined in a simulation study of carcinogenicity experiments. The coverage probabilities of the nonparametric bootstrap method fell below 95% more frequently and by wider margins than the better‐performing parametric bootstrap and likelihood‐based methods. The relative bias of all estimators are seen to be affected by the amount of curvature in the true underlying dose‐response function. In general, the likelihood‐based method has the best coverage probability properties while the parametric bootstrap is less biased and less variable than the likelihood‐based method. Ultimately, neither method is entirely satisfactory for highly curved dose‐response patterns.

Date: 1994
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://doi.org/10.1111/j.1539-6924.1994.tb00069.x

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:riskan:v:14:y:1994:i:6:p:1001-1010

Access Statistics for this article

More articles in Risk Analysis from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().