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Statistical issues in the design analysis and interpretation of animal carcinogenicity studies.

机译:设计分析和解释动物致癌性研究中的统计问题。

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摘要

Statistical issues in the design, analysis and interpretation of animal carcinogenicity studies are discussed. In the area of experimental design, issues that must be considered include randomization of animals, sample size considerations, dose selection and allocation of animals to experimental groups, and control of potentially confounding factors. In the analysis of tumor incidence data, survival differences among groups should be taken into account. It is important to try to distinguish between tumors that contribute to the death of the animal and "incidental" tumors discovered at autopsy in an animal dying of an unrelated cause. Life table analyses (appropriate for lethal tumors) and incidental tumor tests (appropriate for nonfatal tumors) are described, and the utilization of these procedures by the National Toxicology Program is discussed. Despite the fact that past interpretations of carcinogenicity data have tended to focus on pairwise comparisons in general and high-dose effects in particular, the importance of trend tests should not be overlooked, since these procedures are more sensitive than pairwise comparisons to the detection of carcinogenic effects. No rigid statistical "decision rule" should be employed in the interpretation of carcinogenicity data. Although the statistical significance of an observed tumor increase is perhaps the single most important piece of evidence used in the evaluation process, a number of biological factors must also be taken into account. The use of historical control data, the false-positive issue and the interpretation of negative trends are also discussed.
机译:讨论了动物致癌性研究的设计,分析和解释中的统计问题。在实验设计领域,必须考虑的问题包括动物的随机性,样本量的考虑,剂量的选择以及将动物分配给实验组以及控制潜在混杂因素的问题。在分析肿瘤发生率数据时,应考虑各组之间的生存差异。重要的是试图区分导致动物死亡的肿瘤和在死于不相关原因的动物尸体解剖中发现的“偶然”肿瘤。描述了生命表分析(适用于致命性肿瘤)和偶然性肿瘤检测(适用于非致命性肿瘤),并讨论了国家毒理学计划对这些程序的利用。尽管过去对致癌性数据的解释倾向于集中于一般的成对比较,尤其是高剂量效应,但趋势测试的重要性也不容忽视,因为这些程序比成对比较对检测致癌性更为敏感效果。在解释致癌性数据时,不应使用严格的统计“决策规则”。尽管观察到的肿瘤增加的统计意义可能是评估过程中使用的最重要的单一证据,但也必须考虑许多生物学因素。还讨论了历史控制数据的使用,假阳性问题和负面趋势的解释。

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