Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 79-90 |
| Seitenumfang | 12 |
| Fachzeitschrift | Statistics in Biopharmaceutical Research |
| Jahrgang | 5 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 1 Feb. 2013 |
Abstract
This article develops a framework for benchmark dose estimation that allows intrinsically nonlinear dose-response models to be used for continuous data in much the same way as is already possible for quantal data. This means that the same dose-response model equations may be applied to both continuous and quantal data, facilitating benchmark dose estimation in general for a wide range of candidate models commonly used in toxicology. Moreover, the proposed framework provides a convenient means for extending benchmark dose concepts through the use of model averaging and random effects modeling for hierarchical data structures, reflecting increasingly common types of assay data. We illustrate the usefulness of the methodology by means of a cytotoxicology example where the sensitivity of two types of assays are evaluated and compared. By means of a simulation study, we show that the proposed framework provides slightly conservative, yet useful, estimates of benchmark dose lower limit under realistic scenarios.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Statistik und Wahrscheinlichkeit
- Pharmakologie, Toxikologie und Pharmazie (insg.)
- Pharmazeutische Wissenschaften
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in: Statistics in Biopharmaceutical Research, Jahrgang 5, Nr. 1, 01.02.2013, S. 79-90.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - A Unified Framework for Benchmark Dose Estimation Applied to Mixed Models and Model Averaging
AU - Ritz, Christian
AU - Gerhard, Daniel
AU - Hothorn, Ludwig A.
N1 - Funding Information: This work was supported by the ESNATS FP7 project (http://www.esnats.eu). We thank the two reviewers for valuable comments that led to a much improved article.
PY - 2013/2/1
Y1 - 2013/2/1
N2 - This article develops a framework for benchmark dose estimation that allows intrinsically nonlinear dose-response models to be used for continuous data in much the same way as is already possible for quantal data. This means that the same dose-response model equations may be applied to both continuous and quantal data, facilitating benchmark dose estimation in general for a wide range of candidate models commonly used in toxicology. Moreover, the proposed framework provides a convenient means for extending benchmark dose concepts through the use of model averaging and random effects modeling for hierarchical data structures, reflecting increasingly common types of assay data. We illustrate the usefulness of the methodology by means of a cytotoxicology example where the sensitivity of two types of assays are evaluated and compared. By means of a simulation study, we show that the proposed framework provides slightly conservative, yet useful, estimates of benchmark dose lower limit under realistic scenarios.
AB - This article develops a framework for benchmark dose estimation that allows intrinsically nonlinear dose-response models to be used for continuous data in much the same way as is already possible for quantal data. This means that the same dose-response model equations may be applied to both continuous and quantal data, facilitating benchmark dose estimation in general for a wide range of candidate models commonly used in toxicology. Moreover, the proposed framework provides a convenient means for extending benchmark dose concepts through the use of model averaging and random effects modeling for hierarchical data structures, reflecting increasingly common types of assay data. We illustrate the usefulness of the methodology by means of a cytotoxicology example where the sensitivity of two types of assays are evaluated and compared. By means of a simulation study, we show that the proposed framework provides slightly conservative, yet useful, estimates of benchmark dose lower limit under realistic scenarios.
KW - Additional risk
KW - Cytotoxicity
KW - Dose-response modeling
KW - Fractional polynomials
KW - Plate variation
UR - http://www.scopus.com/inward/record.url?scp=84874331187&partnerID=8YFLogxK
U2 - 10.1080/19466315.2012.757559
DO - 10.1080/19466315.2012.757559
M3 - Article
AN - SCOPUS:84874331187
VL - 5
SP - 79
EP - 90
JO - Statistics in Biopharmaceutical Research
JF - Statistics in Biopharmaceutical Research
SN - 1946-6315
IS - 1
ER -