Use of the multistage dose-response model for assessing laboratory animal carcinogenicity
Use of the multistage dose-response model for assessing laboratory animal carcinogenicity
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DOI：
10.1016/j.yrtph.2007.03.002
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发表时间：
2007-07-01
影响因子：
3.4
通讯作者：
West, R. Webster
中科院分区：
文献类型：
--
作者：
Nitcheva, Daniela K.;Piegorsch, Walter W.;West, R. Webster
我们探讨如何以及一个多阶段的统计模型描述剂量反应模式在实验室动物致癌性实验从一个大型数据库的量子响应数据。这些数据是从美国环保署公开的IRIS数据仓库收集的，并进行统计检查，以确定多阶段预测中的高阶值与低阶值相比在解释能力上有多大的改善。我们的研究结果表明，在模型中添加二阶参数只会提高20%的拟合率，而添加更高阶的项显然根本不会对拟合做出贡献，至少对于我们在IRIS数据库中捕获的研究设计来说是这样。还包括一个检查的统计检验，以评估在多阶段剂量反应模型中的高阶项的意义。值得注意的是，bootstrap检验方法似乎比更常见但可能不稳定的“Wald”检验提供了更大的稳定性。(c)2007年爱思唯尔公司All rights reserved.
We explore how well a statistical multistage model describes dose-response patterns in laboratory animal carcinogenicity experiments from a large database of quantal response data. The data are collected from the US EPA's publicly available IRIS data warehouse and examined statistically to determine how often higher-order values in the multistage predictor yield significant improvements in explanatory power over lower-order values. Our results suggest that the addition of a second-order parameter to the model only improves the fit about 20% of the time, while adding even higher-order terms apparently does not contribute to the fit at all, at least with the study designs we captured in the IRIS database. Also included is an examination of statistical tests for assessing significance of higher-order terms in a multistage dose-response model. It is noted that bootstrap testing methodology appears to offer greater stability for performing the hypothesis tests than a more-common, but possibly unstable, "Wald" test. (c) 2007 Elsevier Inc. All rights reserved.