实验动物与比较医学 ›› 2023, Vol. 43 ›› Issue (2): 213-224.DOI: 10.12300/j.issn.1674-5817.2023.043
所属专题: 动物实验设计及统计学方法
王剑1(), 卢今1, 马政文1, 陈国元2, 卢晓3, 白玉4, 刘晓宇5, 卢选成6, 高静7, 李垚1, 庞万勇8(
)(
)
收稿日期:
2023-03-30
修回日期:
2023-04-20
出版日期:
2023-04-25
发布日期:
2023-05-16
通讯作者:
庞万勇(1974—),男,兽医学博士,研究方向:实验动物医学。E-mail: pang1yong@outlook.com。ORCID:0000-0002-0724-2016作者简介:
王 剑(1986—),男,兽医学博士,实验师,研究方向:实验动物医学。E-mail: wangjian3976@shsmu.edu.cn
Jian WANG1(), Jin LU1, Zhengwen MA1, Guoyuan CHEN2, Xiao LU3, Yu BAI4, Xiaoyu LIU5, Xuancheng LU6, Jing GAO7, Yao LI1, Wanyong Pang8(
)(
)
Received:
2023-03-30
Revised:
2023-04-20
Published:
2023-04-25
Online:
2023-05-16
Contact:
PANG Wanyong (ORCID: 0000-0002-0724-2016), E-mail: pang1yong@outlook.com摘要:
提高生物医学研究结果的可重复性是一项重大挑战,研究人员透明且准确地报告其研究过程有利于读者对该研究结果的可靠性进行评估,进而重复该实验或在该成果的基础上进一步探索。ARRIVE 2.0指南是2019年英国国家3Rs中心(NC3Rs)组织发布的一份适用于任何与活体动物研究报告相关的指导性清单,用以提高动物体内实验设计、实验实施和实验报告的规范性,以及动物实验结果的可靠性、可重复性和临床转化率。ARRIVE 2.0指南的使用不仅可以丰富动物实验研究报告的细节,确保动物实验结果信息被充分评估和利用,还可以使读者准确且清晰地了解作者所表述的内容,促进基础研究评审过程的透明化和完整性。目前,ARRIVE 2.0指南已经被国际生物医学期刊广泛采纳。本文是在国际期刊遵循ARRIVE 2.0指南的最佳实践基础上,对2020年发表于PLoS Biology期刊上的ARRIVE 2.0指南完整解读版(原文请见
中图分类号:
王剑, 卢今, 马政文, 陈国元, 卢晓, 白玉, 刘晓宇, 卢选成, 高静, 李垚, 庞万勇. 《动物研究:体内实验报告》即ARRIVE 2.0指南的解释和阐述(一)[J]. 实验动物与比较医学, 2023, 43(2): 213-224.
Jian WANG, Jin LU, Zhengwen MA, Guoyuan CHEN, Xiao LU, Yu BAI, Xiaoyu LIU, Xuancheng LU, Jing GAO, Yao LI, Wanyong Pang. Explanation and Elaboration for the ARRIVE Guidelines 2.0—Reporting Animal Research and In Vivo Experiments (Ⅰ)[J]. Laboratory Animal and Comparative Medicine, 2023, 43(2): 213-224.
术语名称 Terminology name | 含义 Content |
---|---|
偏倚 Bias | 对干预的真实效果的过高或过低估计。偏倚是由实验设计、实施或分析的不足引起,从而导致误差的引入 |
统计描述和统计推断 Descriptive and inferential statistics | 统计描述用于总结数据,通常包括集中趋势(如平均值或中位数)的测量和离散程度(如标准差或范围)的测量。统计推断用于对从中抽取样本的总体进行概括。假设检验如方差分析(ANOVA)、Mann-Whitney检验或t检验等属于统计推断的范畴 |
效应量 Effect size | 组间差异或变量之间关系强度的定量测量 |
实验单元 Experimental unit | 独立于所有其他单元而接受干预的生物实体,这样就可以将任何两个实验单元分配给不同的处理组。有时也被称为随机化单元。 |
外部效度 External validity | 某一特定研究的结果能够应用或推广到其他研究、研究条件、动物品系/物种或人类的程度 |
假阴性 False negative | 当备择假设(H1)为真时,却得到无统计学意义的结果。在统计学中,它被称为第Ⅱ类错误 |
假阳性 False positive | 当零假设(H0)为真时,却得到具有统计学意义的结果。在统计学中,它被称为第Ⅰ类错误 |
自变量 Independent variable | 研究人员控制的变量(如处理、条件、时间)或样本的属性(如性别)或技术特征(如批次、笼、样本收集)等可能会影响结果测量的变量。自变量可以是科研中所关注的变量,也可以是干扰变量。自变量也被称为预测变量 |
内部效度 Internal validity | 某一特定研究的结果在多大程度上可以归因于实验干预的效果,而不是其他一些未知的因素(如该研究的设计、实施或分析的不足所引入的偏倚) |
干扰变量 Nuisance variable | 干扰变量是指在研究中出现的一类变量,不是研究的主要关注点,但可能对研究结果测量产生影响并增加变异性,因此需要在实验设计或分析中加以考虑。此外,如果它们与关注的自变量有关联,就成为混杂因素,因为这会引入偏倚。在实验设计(以防止它们成为混杂因素)和分析(解释变异性,有时是降低偏倚)中都应对干扰变量加以考虑。 例如,可将干扰变量作为区组因素或协变量 |
零假设和备择假设 Null and alternative hypotheses | 零假设(H0)是指没有影响/效应,如各组之间的差异或变量之间的关联。备择假设(H1)是假设存在某种效应 |
结果测量/结局变量 Outcome measure | 在研究过程中记录的、用以评估处理或实验干预效果的任何变量。它也被称为因变量、响应变量 |
统计效力/检验效能 Power | 对于预先定义的有生物学意义的效应量,如果效应真实存在(即零假设被正确拒绝),统计检验将检测出效应的概率 |
样本量 Sample size | 每组的实验单元数量,也被称为n |
表1 ARRIVE 2.0指南所附统计学术语
Table 1 Statistical terminology attached to the ARRIVE 2.0 guidelines
术语名称 Terminology name | 含义 Content |
---|---|
偏倚 Bias | 对干预的真实效果的过高或过低估计。偏倚是由实验设计、实施或分析的不足引起,从而导致误差的引入 |
统计描述和统计推断 Descriptive and inferential statistics | 统计描述用于总结数据,通常包括集中趋势(如平均值或中位数)的测量和离散程度(如标准差或范围)的测量。统计推断用于对从中抽取样本的总体进行概括。假设检验如方差分析(ANOVA)、Mann-Whitney检验或t检验等属于统计推断的范畴 |
效应量 Effect size | 组间差异或变量之间关系强度的定量测量 |
实验单元 Experimental unit | 独立于所有其他单元而接受干预的生物实体,这样就可以将任何两个实验单元分配给不同的处理组。有时也被称为随机化单元。 |
外部效度 External validity | 某一特定研究的结果能够应用或推广到其他研究、研究条件、动物品系/物种或人类的程度 |
假阴性 False negative | 当备择假设(H1)为真时,却得到无统计学意义的结果。在统计学中,它被称为第Ⅱ类错误 |
假阳性 False positive | 当零假设(H0)为真时,却得到具有统计学意义的结果。在统计学中,它被称为第Ⅰ类错误 |
自变量 Independent variable | 研究人员控制的变量(如处理、条件、时间)或样本的属性(如性别)或技术特征(如批次、笼、样本收集)等可能会影响结果测量的变量。自变量可以是科研中所关注的变量,也可以是干扰变量。自变量也被称为预测变量 |
内部效度 Internal validity | 某一特定研究的结果在多大程度上可以归因于实验干预的效果,而不是其他一些未知的因素(如该研究的设计、实施或分析的不足所引入的偏倚) |
干扰变量 Nuisance variable | 干扰变量是指在研究中出现的一类变量,不是研究的主要关注点,但可能对研究结果测量产生影响并增加变异性,因此需要在实验设计或分析中加以考虑。此外,如果它们与关注的自变量有关联,就成为混杂因素,因为这会引入偏倚。在实验设计(以防止它们成为混杂因素)和分析(解释变异性,有时是降低偏倚)中都应对干扰变量加以考虑。 例如,可将干扰变量作为区组因素或协变量 |
零假设和备择假设 Null and alternative hypotheses | 零假设(H0)是指没有影响/效应,如各组之间的差异或变量之间的关联。备择假设(H1)是假设存在某种效应 |
结果测量/结局变量 Outcome measure | 在研究过程中记录的、用以评估处理或实验干预效果的任何变量。它也被称为因变量、响应变量 |
统计效力/检验效能 Power | 对于预先定义的有生物学意义的效应量,如果效应真实存在(即零假设被正确拒绝),统计检验将检测出效应的概率 |
样本量 Sample size | 每组的实验单元数量,也被称为n |
图2 文献[32]中每组动物的处理和移植方案注:*表示这些组最初有12只动物,但一些动物患上了与实验无关的肿瘤,因此被从研究中移除。
Figure 2 The scheme of treatment and transplantation for each group of animals in study [32]Note:*These groups initially consisted of 12 animals, but some animals developed tumors unrelated to the experiment and were therefore removed from the study.
图3 文献[59]中实验方案及其所使用、死亡和纳入的动物数量
Figure 3 Flow chart showing the experimental protocol with the number of animals used, died and included in the study [59]
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