Is the assumption of normality or log-normality for continuous response data critical for benchmark dose estimation?
- ORISE Postdoctoral Fellow, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC (United States)
- National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC (United States)
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC (United States)
Continuous responses (e.g. body weight) are widely used in risk assessment for determining the benchmark dose (BMD) which is used to derive a U.S. EPA reference dose. One critical question that is not often addressed in dose–response assessments is whether to model the continuous data as normally or log-normally distributed. Additionally, if lognormality is assumed, and only summarized response data (i.e., mean ± standard deviation) are available as is usual in the peer-reviewed literature, the BMD can only be approximated. In this study, using the “hybrid” method and relative deviation approach, we first evaluate six representative continuous dose–response datasets reporting individual animal responses to investigate the impact on BMD/BMDL estimates of (1) the distribution assumption and (2) the use of summarized versus individual animal data when a log-normal distribution is assumed. We also conduct simulation studies evaluating model fits to various known distributions to investigate whether the distribution assumption has influence on BMD/BMDL estimates. Our results indicate that BMDs estimated using the hybrid method are more sensitive to the distribution assumption than counterpart BMDs estimated using the relative deviation approach. The choice of distribution assumption has limited impact on the BMD/BMDL estimates when the within dose-group variance is small, while the lognormality assumption is a better choice for relative deviation method when data are more skewed because of its appropriateness in describing the relationship between mean and standard deviation. Additionally, the results suggest that the use of summarized data versus individual response data to characterize log-normal distributions has minimal impact on BMD estimates. - Highlights: • We investigate to what extent the distribution assumption can affect BMD estimates. • Both real data analysis and simulation study are conducted. • BMDs estimated using hybrid method are more sensitive to distribution assumption. • Summarized continuous data are adequate for BMD estimation.
- OSTI ID:
- 22285468
- Journal Information:
- Toxicology and Applied Pharmacology, Vol. 272, Issue 3; Other Information: Copyright (c) 2013 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0041-008X
- Country of Publication:
- United States
- Language:
- English
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