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Multiplicity-Adjusted Confidence Limits in Risk Assessment with Quantal Response Data.
- Source :
-
Journal of Biopharmaceutical Statistics . 2018, Vol. 28 Issue 6, p1182-1192. 11p. 7 Charts, 1 Graph. - Publication Year :
- 2018
-
Abstract
- In risk assessment, it is often desired to make inferences on the risk at certain low doses or on the dose(s) at which a specific benchmark risk (BMR) is attained. At times, dose levels or BMRs are of interest, and some form of multiplicity adjustment is necessary to ensure a valid simultaneous inference. Bonferroni correction is often employed in practice for such purposes. Though relative simple to implement, the Bonferroni strategy can suffer from extreme conservatism (Nitcheva et al., 2005; Al-Saidy et al., 2003). Recently, Kerns (2017) proposed the use of simultaneous hyperbolic and three-segment bands to perform multiple inferences in risk assessment under Abbott-adjusted log-logistic model with the dose level constrained to a given interval. In this paper, we present and compare methods for deriving multiplicity-adjusted upper limits on extra risk and lower bounds on the benchmark dose under Abbott-adjusted log-logistic model. Monte Carlo simulations evaluate the characteristics of the simultaneous limits. An example is given to illustrate the use of the methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10543406
- Volume :
- 28
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Journal of Biopharmaceutical Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 132836075
- Full Text :
- https://doi.org/10.1080/10543406.2018.1452026