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一个共同的对照组——优化实验设计以最大化灵敏度。

A common control group - optimising the experiment design to maximise sensitivity.

作者信息

Bate Simon, Karp Natasha A

机构信息

Statistical Science Europe, GlaxoSmithKline Pharmaceuticals, Stevenage, United Kingdom.

Mouse Informatics Group, Wellcome Trust Sanger Institute, Cambridge, United Kingdom.

出版信息

PLoS One. 2014 Dec 11;9(12):e114872. doi: 10.1371/journal.pone.0114872. eCollection 2014.

Abstract

Methods for choosing an appropriate sample size in animal experiments have received much attention in the statistical and biological literature. Due to ethical constraints the number of animals used is always reduced where possible. However, as the number of animals decreases so the risk of obtaining inconclusive results increases. By using a more efficient experimental design we can, for a given number of animals, reduce this risk. In this paper two popular cases are considered, where planned comparisons are made to compare treatments back to control and when researchers plan to make all pairwise comparisons. By using theoretical and empirical techniques we show that for studies where all pairwise comparisons are made the traditional balanced design, as suggested in the literature, maximises sensitivity. For studies that involve planned comparisons of the treatment groups back to the control group, which are inherently more sensitive due to the reduced multiple testing burden, the sensitivity is maximised by increasing the number of animals in the control group while decreasing the number in the treated groups.

摘要

在动物实验中选择合适样本量的方法在统计学和生物学文献中受到了广泛关注。由于伦理限制,所用动物数量总是尽可能减少。然而,随着动物数量的减少,获得不确定结果的风险增加。通过使用更有效的实验设计,对于给定数量的动物,我们可以降低这种风险。本文考虑了两种常见情况,一种是进行计划比较以将治疗与对照进行对比,另一种是研究人员计划进行所有成对比较。通过使用理论和实证技术,我们表明,对于进行所有成对比较的研究,如文献中所建议的传统平衡设计可使灵敏度最大化。对于涉及将治疗组与对照组进行计划比较的研究,由于多重检验负担减轻,其本质上更具灵敏度,通过增加对照组中的动物数量同时减少治疗组中的动物数量可使灵敏度最大化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e643/4263717/d62241ee9601/pone.0114872.g001.jpg

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