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毒理学研究中啮齿动物生长数据的分析。

Analysis of rodent growth data in toxicology studies.

作者信息

Hoffman Wherly P, Ness Daniel K, van Lier Robert B L

机构信息

Statistics and Information Sciences, Lilly Research Laboratories, Eli Lilly and Company, Drop Code GL43, 2001 West Main Street, Greenfield, Indiana 46140, USA.

出版信息

Toxicol Sci. 2002 Apr;66(2):313-9. doi: 10.1093/toxsci/66.2.313.

Abstract

To evaluate compound-related effects on the growth of rodents, body weight and food consumption data are commonly collected either weekly or biweekly in toxicology studies. Body weight gain, food consumption relative to body weight, and efficiency of food utilization can be derived from body weight and food consumption for each animal in an attempt to better understand the compound-related effects. These five parameters are commonly analyzed in toxicology studies for each sex using a one-factor analysis of variance (ANOVA) at each collection point. The objective of this manuscript is to present an alternative approach to the evaluation of compound-related effects on body weight and food consumption data from both subchronic and chronic rodent toxicology studies. This approach is to perform a repeated-measures ANOVA on a selected set of parameters and analysis intervals. Compared with a standard one-factor ANOVA, this approach uses a statistical analysis method that has greater power and reduces the number of false-positive claims, and consequently provides a succinct yet comprehensive summary of the compound-related effects. Data from a mouse carcinogenicity study are included to illustrate this repeated-measures ANOVA approach to analyzing growth data in contrast with the one-factor ANOVA approach.

摘要

为评估化合物对啮齿动物生长的影响,在毒理学研究中通常每周或每两周收集一次体重和食物消耗数据。体重增加、相对于体重的食物消耗量以及食物利用效率可从每只动物的体重和食物消耗数据中得出,以便更好地了解与化合物相关的影响。在毒理学研究中,通常在每个收集点对每种性别使用单因素方差分析(ANOVA)来分析这五个参数。本手稿的目的是提出一种替代方法,用于评估亚慢性和慢性啮齿动物毒理学研究中与化合物相关的对体重和食物消耗数据的影响。该方法是对一组选定的参数和分析间隔进行重复测量方差分析。与标准的单因素方差分析相比,该方法使用的统计分析方法具有更强的功效,减少了假阳性结果的数量,因此能够简洁而全面地总结与化合物相关的影响。文中纳入了一项小鼠致癌性研究的数据,以说明这种重复测量方差分析方法与单因素方差分析方法相比在分析生长数据方面的应用。

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