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Study design: think 'scientific value' not 'p-values'. 研究设计:要考虑 "科学价值",而不是 "P 值"。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 DOI: 10.1177/00236772241276806
Penny S Reynolds

Statistically based experimental designs have been available for over a century. However, many preclinical researchers are completely unaware of these methods, and the success of experiments is usually equated only with 'p < 0.05'. By contrast, a well-thought-out experimental design strategy provides data with evidentiary and scientific value. A value-based strategy requires implementation of statistical design principles coupled with basic project management techniques. This article outlines the three phases of a value-based design strategy: proper framing of the research question, statistically based operationalisation through careful selection and structuring of appropriate inputs, and incorporation of methods that minimise bias and process variation. Appropriate study design increases study validity and the evidentiary strength of the results, reduces animal numbers, and reduces waste from noninformative experiments. Statistically based experimental design is thus a key component of the 'Reduction' pillar of the 3R (Replacement, Reduction, Refinement) principles for ethical animal research.

以统计学为基础的实验设计已有一个多世纪的历史。然而,许多临床前研究人员完全不了解这些方法,而且实验的成功与否通常只等同于 "p"。
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引用次数: 0
Methods for applying blinding and randomisation in animal experiments. 在动物实验中应用盲法和随机化的方法。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 DOI: 10.1177/00236772241272991
P S Verhave, R van Eenige, Iacw Tiebosch

Blinding and randomisation are important methods for increasing the robustness of pre-clinical studies, as incomplete or improper implementation thereof is recognised as a source of bias. Randomisation ensures that any known and unknown covariates introducing bias are randomly distributed over the experimental groups. Thereby, differences between the experimental groups that might otherwise have contributed to false positive or -negative results are diminished. Methods for randomisation range from simple randomisation (e.g. rolling a dice) to advanced randomisation strategies involving the use of specialised software. Blinding on the other hand ensures that researchers are unaware of group allocation during the preparation, execution and acquisition and/or the analysis of the data. This minimises the risk of unintentional influences resulting in bias. Methods for blinding require strong protocols and a team approach. In this review, we outline methods for randomisation and blinding and give practical tips on how to implement them, with a focus on animal studies.

盲法和随机化是提高临床前研究稳健性的重要方法,因为盲法和随机化的不完全或不当实施被认为是偏差的来源。随机化可确保任何会产生偏差的已知和未知协变量在实验组中随机分布。这样,实验组之间的差异就会减少,否则可能会造成假阳性或假阴性结果。随机化的方法多种多样,从简单的随机化(如掷骰子)到使用专门软件的高级随机化策略,不一而足。另一方面,盲法可确保研究人员在准备、执行、获取和/或分析数据期间不知道组别分配情况。这就最大限度地降低了无意影响导致偏差的风险。盲法需要强有力的协议和团队合作。在这篇综述中,我们概述了随机化和盲法的方法,并以动物研究为重点,提供了如何实施这些方法的实用技巧。
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引用次数: 0
About statistical significance, and the lack thereof. 关于统计意义,以及缺乏统计意义的问题。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-08-19 DOI: 10.1177/00236772241248509
Fulvio Magara, Benjamin Boury-Jamot

Absence of statistical significance (i.e., p > 0.05) in the results of a frequentist test comparing two samples is often used as evidence of absence of difference, or absence of effect of a treatment, on the measured variable. Such conclusions are often wrong because absence of significance may merely result from a sample size that is too small to reveal an effect. To conclude that there is no meaningful effect of a treatment/condition, it is necessary to use an appropriate statistical approach. For frequentist statistics, a simple tool for this goal is the 'two one-sided t-test,' a form of equivalence test that relies on the a priori definition of a minimal difference considered to be relevant. In other words, the smallest effect size of interest should be established in advance. We present the principles of this test and give examples where it allows correct interpretation of the results of a classical t-test assuming absence of difference. Equivalence tests are also very useful in probing whether certain significant results are also biologically meaningful, because when comparing large samples it is possible to find significant results in both an equivalence test and in a two-sample t-test, assuming no difference as the null hypothesis.

在比较两个样本的频数检验结果中,如果没有统计学意义(即 p > 0.05),通常会被用来证明在测量的变量上没有差异,或治疗没有效果。这种结论往往是错误的,因为没有显著性可能只是因为样本量太小,无法显示效果。要得出某项治疗/条件不存在有意义影响的结论,必须使用适当的统计方法。对于频数统计来说,实现这一目标的简单工具是 "两个单侧 t 检验",这是一种等效检验,它依赖于被认为相关的最小差异的先验定义。换句话说,应事先确定相关的最小效应大小。我们介绍了这种检验的原理,并举例说明了在假设无差异的情况下,它可以正确解释经典 t 检验的结果。等效检验在探究某些显著结果是否也具有生物学意义方面也非常有用,因为在比较大样本时,假设无差异为零假设,在等效检验和双样本 t 检验中都有可能发现显著结果。
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引用次数: 0
Depicting variability and uncertainty using intervals and error bars. 使用区间和误差条描述可变性和不确定性。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-09-05 DOI: 10.1177/00236772241247105
Naomi Altman, Martin Krzywinski

Variability is inherent in most biological systems due to differences among members of the population. Two types of variation are commonly observed in studies: differences among samples and the "error" in estimating a population parameter (e.g. mean) from a sample. While these concepts are fundamentally very different, the associated variation is often expressed using similar notation-an interval that represents a range of values with a lower and upper bound. In this article we discuss how common intervals are used (and misused).

由于种群成员之间的差异,大多数生物系统都存在固有的变异性。研究中通常会观察到两种类型的变异:样本间的差异和根据样本估计群体参数(如平均值)时的 "误差"。虽然这两个概念从根本上说非常不同,但相关的变异通常使用类似的符号来表示--一个区间,代表一个有下限和上限的数值范围。本文将讨论常见区间的使用(和误用)方式。
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引用次数: 0
Ditching the norm: Using alternative distributions for biological data analysis. 抛弃常规:使用替代分布进行生物数据分析
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-08-19 DOI: 10.1177/00236772241246602
Stanley E Lazic

Most classical statistical tests assume data are normally distributed. If this assumption is not met, researchers often turn to non-parametric methods. These methods have some drawbacks, and if no suitable non-parametric test exists, a normal distribution may be used inappropriately instead. A better option is to select a distribution appropriate for the data from dozens available in modern software packages. Selecting a distribution that represents the data generating process is a crucial but overlooked step in analysing data. This paper discusses several alternative distributions and the types of data that they are suitable for.

大多数经典统计检验都假设数据呈正态分布。如果不符合这一假设,研究人员通常会求助于非参数方法。这些方法有一些缺点,如果没有合适的非参数检验方法,可能会不恰当地使用正态分布。更好的选择是从现代软件包中的几十种分布中选择适合数据的分布。选择一个能代表数据生成过程的分布是分析数据的关键步骤,但却被忽视了。本文将讨论几种可供选择的分布及其适用的数据类型。
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引用次数: 0
Understanding p-values and significance. 了解 p 值和显著性。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-09-24 DOI: 10.1177/00236772241247106
Naomi Altman, Martin Krzywinski

P-values combined with estimates of effect size are used to assess the importance of experimental results. However, their interpretation can be invalidated by selection bias when testing multiple hypotheses, fitting multiple models or even informally selecting results that seem interesting after observing the data. We offer an introduction to principled uses of p-values (targeted at the non-specialist) and identify questionable practices to be avoided.

P 值与效应大小估计值相结合,用于评估实验结果的重要性。然而,在测试多个假设、拟合多个模型或甚至在观察数据后非正式地选择看起来有趣的结果时,其解释可能会因选择偏差而失效。我们将介绍 p 值的原则性用法(针对非专业人士),并指出应避免的可疑做法。
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引用次数: 0
Testing for normality: a user's (cautionary) guide. 正态性测试:用户(警示)指南。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 DOI: 10.1177/00236772241276808
Romain-Daniel Gosselin

The normality assumption postulates that empirical data derives from a normal (Gaussian) population. It is a pillar of inferential statistics that enables the theorization of probability functions and the computation of p-values thereof. The breach of this assumption may not impose a formal mathematical constraint on the computation of inferential outputs (e.g., p-values) but may make them inoperable and possibly lead to unethical waste of laboratory animals. Various methods, including statistical tests and qualitative visual examination, can reveal incompatibility with normality and the choice of a procedure should not be trivialized. The following minireview will provide a brief overview of diagrammatical methods and statistical tests commonly employed to evaluate congruence with normality. Special attention will be given to the potential pitfalls associated with their application. Normality is an unachievable ideal that practically never accurately describes natural variables, and detrimental consequences of non-normality may be safeguarded by using large samples. Therefore, the very concept of preliminary normality testing is also, arguably provocatively, questioned.

正态性假设假定经验数据来自正常(高斯)群体。它是推理统计的支柱,使概率函数的理论化及其 p 值的计算成为可能。违反这一假设可能不会对推理输出(如 p 值)的计算造成正式的数学限制,但可能会使其无法操作,并可能导致实验动物的不道德浪费。各种方法,包括统计检验和定性直观检查,都可以发现不符合正态性的情况,因此不应轻视程序的选择。下面的小视图将简要介绍常用于评估是否符合正态性的图解法和统计检验法。我们将特别关注与这些方法的应用相关的潜在隐患。正态性是一个无法实现的理想,实际上永远无法准确描述自然变量,而使用大样本可以避免非正态性的有害后果。因此,初步正态性检验的概念本身也受到了质疑,可以说是挑衅性的质疑。
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引用次数: 0
Laboratory Animals presents Special Issue Biostatistics Notes. 实验动物》推出特刊《生物统计笔记》。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 DOI: 10.1177/00236772241273059
Romain-Daniel Gosselin, Penny S Reynolds
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引用次数: 0
Bayesian statistical concepts with examples from rodent toxicology studies. 贝叶斯统计概念与啮齿动物毒理学研究实例。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-09-20 DOI: 10.1177/00236772241262829
Gary J Larson, Keith R Shockley

The theory and practice of statistics comprises two main schools of thought: frequentist statistics and Bayesian statistics. Frequentist methods are most commonly used to analyze animal-based laboratory data, while Bayesian statistical methods have been implemented less widely and may be relatively unfamiliar to practitioners in experimental science. This paper provides a high-level overview of Bayesian statistics and how they compare with frequentist methods. Using examples in rodent toxicity research, we argue that Bayesian methods have much to offer laboratory animal researchers. We advocate for increased attention to and adoption of Bayesian methods in laboratory animal research. Bayesian statistical theory, methods, software, and education have advanced significantly in the last 30 years, making these tools more accessible than ever.

统计学的理论和实践包括两大流派:频数统计和贝叶斯统计。频数统计方法最常用于分析基于动物的实验室数据,而贝叶斯统计方法的应用范围较小,实验科学从业人员可能相对陌生。本文简要介绍了贝叶斯统计方法及其与频数法的比较。通过啮齿动物毒性研究中的实例,我们认为贝叶斯方法可以为实验动物研究人员提供很多帮助。我们主张在实验动物研究中更多地关注和采用贝叶斯方法。贝叶斯统计理论、方法、软件和教育在过去 30 年中取得了长足的进步,使这些工具比以往任何时候都更容易获得。
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引用次数: 0
Half the price, twice the gain: How to simultaneously decrease animal numbers and increase precision with good experimental design. 价格减半,收益加倍:如何通过良好的实验设计同时减少动物数量和提高精确度。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-09-24 DOI: 10.1177/00236772241260905
Servan Luciano Grüninger, Florian Frommlet

Animal research often involves experiments in which the effect of several factors on a particular outcome is of scientific interest. Many researchers approach such experiments by varying just one factor at a time. As a consequence, they design and analyze the experiments based on a pairwise comparison between two groups. However, this approach uses unreasonably large numbers of animals and leads to severe limitations in terms of the research questions that can be answered. Factorial designs and analyses offer a more efficient way to perform and assess experiments with multiple factors of interest. We will illustrate the basic principles behind these designs, discussing a simple example with only two factors before suggesting how to design and analyze more complex experiments involving larger numbers of factors based on multiway analysis of variance.

动物研究经常涉及到一些实验,在这些实验中,多个因素对某一特定结果的影响会引起科学兴趣。许多研究人员在进行此类实验时,每次只改变一个因素。因此,他们根据两组之间的配对比较来设计和分析实验。然而,这种方法使用了大量不合理的动物,导致在回答研究问题方面受到严重限制。因子设计和分析提供了一种更有效的方法来执行和评估具有多个相关因子的实验。我们将说明这些设计背后的基本原理,先讨论一个只有两个因子的简单例子,然后建议如何根据多向方差分析设计和分析涉及更多因子的更复杂实验。
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Laboratory Animals
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