首页> 外文期刊>Regulatory Toxicology and Pharmacology: RTP >Statistical power in the analyses of brain weight measures in pesticide neurotoxicity testing and the relationship between brain and body weight.
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Statistical power in the analyses of brain weight measures in pesticide neurotoxicity testing and the relationship between brain and body weight.

机译:农药神经毒性测试中脑重量测量值的分析以及脑与体重之间关系的统计能力。

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Few investigations have examined statistical power in studies of pesticide exposure effects on rat brain weight. Using data from developmental neurotoxicity studies conducted by four different laboratories, we evaluated statistical power to detect changes in rat brain weight and examined the relationship between brain and body weight in several age groups. All power calculations assumed an alpha value of 0.05, equal variance between groups, and a sample size of 10/group. Statistical power often varied substantially both within and between laboratories, and for a 5% change in brain weight average between-laboratory differences in power were as large as 34%. Power estimates for individual studies often spanned a range of more than 50% within a given laboratory. The likelihood of detecting a "significant" change smaller than 5% was generally low for all laboratories. Brain weight increased linearly with body weight within age groups but the strength of this relationship decreased with age. Therefore, confounding by body weight may be more likely in brain weight analyses for younger animals. Our findings suggest that caution is required when weighing the importance of statistical "significance" in studies of pesticide exposure effects on brain weight as the power to detect these differences may be low.
机译:很少有研究检验农药暴露对大鼠脑重量的影响的统计功效。使用来自四个不同实验室进行的发育性神经毒性研究的数据,我们评估了检测老鼠大脑重量变化的统计能力,并检查了多个年龄组的大脑与体重之间的关系。所有功效计算均假设alpha值为0.05,组之间方差相等,样本大小为10 /组。在实验室内部和实验室之间,统计能力经常有很大的不同,而实验室之间的平均体重差异为5%时,实验室之间的能力差异高达34%。在给定的实验室中,单个研究的功效估计通常跨越50%以上的范围。对于所有实验室而言,检测到小于5%的“重大”变化的可能性通常较低。在年龄组内,脑重量随体重线性增加,但这种关系的强度随年龄下降。因此,在较年轻的动物的大脑重量分析中,体重混淆的可能性更大。我们的发现表明,在研究农药暴露对大脑重量的影响时,权衡统计“显着性”的重要性时需要谨慎,因为检测这些差异的能力可能很低。

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