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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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引用次数: 0
Our 61st Annual Meeting: An exciting programme is shaping up! 我们的第 61 届年会:令人兴奋的活动即将开始!
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 DOI: 10.1177/00236772241279473
Jordi L Tremoleda
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引用次数: 0
Preclinical pilot studies: Five common pitfalls and how to avoid them. 临床前试验研究:五个常见陷阱及如何避免。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-08-05 DOI: 10.1177/00236772241244519
Natasha A Karp, Alan Sharpe, Benjamin Phillips

Pilots are small-scale initial experiments that are intended to guide the design of future, larger studies, with a view to increasing their effectiveness. In this statistical primer we highlight five common mistakes that limit the utility of pilot studies and provide practical guidance to avoid such errors and increase their effectiveness. The common thread connecting these mistakes is insufficient planning and over-interpretation of the results. This approach compromises the ultimate goals of the research programme and the future experimental cascade. In support of our view that over-interpretation is an error, we present a simple simulation to demonstrate that pilots will generally generate an inaccurate estimate of the variability of the biological endpoint under study and that frequent under-estimation will lead to inconclusive and unethical subsequent experiments. We argue that well planned pilots are an important part of the research cascade and still need to be implemented to a high standard.

试点研究是小规模的初步实验,旨在指导未来更大规模研究的设计,从而提高研究的有效性。在这本统计入门书中,我们强调了限制试验研究效用的五个常见错误,并提供了避免这些错误和提高试验研究效用的实用指导。这些错误的共同点是计划不足和过度解读结果。这种做法损害了研究计划的最终目标和未来的实验级联。为了支持我们的观点,即过度解读是一种错误,我们提出了一个简单的模拟,以证明试验通常会对所研究的生物终点的变异性产生不准确的估计,而经常性的估计不足会导致后续实验的不确定和不道德。我们认为,计划周密的试验是研究级联的重要组成部分,但仍需以高标准加以实施。
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引用次数: 0
Treatment randomisation at animal or pen level? : Statistical analysis should follow the randomisation pattern! 在动物或栏的层面上进行治疗随机化? 统计分析应遵循随机化模式!
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-08-19 DOI: 10.1177/00236772241247274
Luc Duchateau, Robrecht Dockx, Klara Goethals, Matthijs Vynck, Frédéric Vangroenweghe, Christian Burvenich

Random treatment assignment is essential in demonstrating a causal relationship between a treatment and the outcome of interest. Randomisation ensures that animals assigned to different treatment groups do not differ from each other systematically, except for the randomly assigned treatment. The randomisation pattern should also dictate the statistical analysis.

随机治疗分配对于证明治疗与相关结果之间的因果关系至关重要。随机分配可确保被分配到不同治疗组的动物之间除了随机分配的治疗外没有系统性差异。随机分配模式还应决定统计分析。
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引用次数: 0
Vacancy for EDITOR position to join the EIC team. 编辑职位空缺,请加入 EIC 团队。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-09-20 DOI: 10.1177/00236772241281044
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引用次数: 0
Heterogeneity of animal experiments and how to deal with it. 动物实验的异质性及应对方法。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-09-24 DOI: 10.1177/00236772241260173
Bernhard Voelkl, Hanno Würbel

Heterogeneity of study samples is ubiquitous in animal experiments. Here, we discuss the different options of how to deal with heterogeneity in the statistical analysis of a single experiment. Specifically, data from different sub-groups (e.g. sex, strain, age cohorts) may be analysed separately, heterogenization factors may be ignored and data pooled for analysis, or heterogenization factors may be included as additional variables in the statistical model. The cost of ignoring a heterogenization factor is an inflated estimate of the variance and a consequent loss of statistical power. Therefore, it is usually preferable to include the heterogenization factor in the statistical model, especially if the heterogenization factor has been introduced intentionally (e.g. using both sexes). If heterogenization factors are included, they can be treated either as fixed factors in an analysis of variance design or sometimes as random effects in mixed effects regression models. Finally, for an appropriate sample size estimation, it is necessary to decide whether to treat heterogenization factors as nuisance variables, or whether the experiment should be powered to be able to detect not only the main effect of the treatment but also interactions between heterogenization factors and the treatment variable.

研究样本的异质性在动物实验中无处不在。在此,我们将讨论如何在单个实验的统计分析中处理异质性的不同方案。具体来说,可以分别分析来自不同亚组(如性别、品系、年龄组)的数据,也可以忽略异质性因素,将数据集中起来进行分析,还可以将异质性因素作为额外变量纳入统计模型。忽略异质化因素的代价是夸大方差估计值,从而丧失统计能力。因此,通常最好在统计模型中加入异质化因子,尤其是在有意引入异质化因子的情况下(如使用两性)。如果加入了异质化因子,可以在方差分析设计中将其作为固定因子处理,有时也可以在混合效应回归模型中将其作为随机效应处理。最后,为了估算出适当的样本量,有必要决定是否将异质化因素视为干扰变量,或者是 否应该为实验提供动力,以便不仅能够检测出处理的主要效应,而且能够检测出异质化因素 与处理变量之间的交互作用。
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引用次数: 0
Incorporating sources of correlation between outcomes: An introduction to mixed models. 纳入结果之间的相关性来源:混合模型简介。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-09-20 DOI: 10.1177/00236772241259518
Limeng Liu, Ashley Petersen

Animal research often involves measuring the outcomes of interest multiple times on the same animal, whether over time or for different exposures. These repeated outcomes measured on the same animal are correlated due to animal-specific characteristics. While this repeated measures data can address more complex research questions than single-outcome data, the statistical analysis must take into account the study design resulting in correlated outcomes, which violate the independence assumption of standard statistical methods (e.g. a two-sample t-test, linear regression). When standard statistical methods are incorrectly used to analyze correlated outcome data, the statistical inference (i.e. confidence intervals and p-values) will be incorrect, with some settings leading to null findings too often and others producing statistically significant findings despite no support for this in the data. Instead, researchers can leverage approaches designed specifically for correlated outcomes. In this article, we discuss common study designs that lead to correlated outcome data, motivate the intuition about the impact of improperly analyzing correlated outcomes using methods for independent data, and introduce approaches that properly leverage correlated outcome data.

动物研究通常涉及在同一动物身上多次测量感兴趣的结果,无论是随时间推移还是针对不同的暴露。由于动物的特异性,在同一动物身上重复测量的结果具有相关性。虽然与单一结果数据相比,重复测量数据可以解决更复杂的研究问题,但统计分析必须考虑到研究设计导致的相关结果,这违反了标准统计方法(如双样本 t 检验、线性回归)的独立性假设。如果不正确地使用标准统计方法来分析相关结果数据,统计推断(即置信区间和 p 值)将是不正确的,有些设置往往会导致无效结果,而有些设置则会产生具有统计意义的结果,尽管数据中并不支持这种结果。相反,研究人员可以利用专为相关结果设计的方法。在本文中,我们将讨论导致相关结果数据的常见研究设计,激发对使用独立数据方法不当分析相关结果的影响的直觉,并介绍正确利用相关结果数据的方法。
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引用次数: 0
Causal mediation analysis: How to avoid fooling yourself that X causes Y. 因果中介分析:如何避免自欺欺人地认为 X 会导致 Y。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-08-11 DOI: 10.1177/00236772231217777
Stanley E Lazic

The purpose of many preclinical studies is to determine whether an experimental intervention affects an outcome through a particular mechanism, but the analytical methods and inferential logic typically used cannot answer this question, leading to erroneous conclusions about causal relationships, which can be highly reproducible. A causal mediation analysis can directly test whether a hypothesised mechanism is partly or completely responsible for a treatment's effect on an outcome. Such an analysis can be easily implemented with modern statistical software. We show how a mediation analysis can distinguish between three different causal relationships that are indistinguishable when using a standard analysis.

许多临床前研究的目的是确定实验干预是否通过特定机制影响结果,但通常使用的分析方法和推理逻辑无法回答这一问题,从而导致得出错误的因果关系结论,而这种因果关系的可重复性很高。因果中介分析可以直接检验假设的机制是否对治疗对结果的影响负部分或全部责任。这种分析可以通过现代统计软件轻松实现。我们展示了因果中介分析如何区分三种不同的因果关系,而这三种关系在使用标准分析时是无法区分的。
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引用次数: 0
Simulation methodologies to determine statistical power in laboratory animal research studies. 确定实验动物研究统计能力的模拟方法。
IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Pub Date : 2024-10-01 Epub Date: 2024-09-24 DOI: 10.1177/00236772241273002
Angela Jeffers, Kathryn Konrad, Gary Larson, Katherine Allen-Moyer, Helen Cunny, Keith Shockley

Null hypothesis significance testing is a statistical tool commonly employed throughout laboratory animal research. When experimental results are reported, the reproducibility of the results is of utmost importance. Establishing standard, robust, and adequately powered statistical methodology in the analysis of laboratory animal data is critical to ensure reproducible and valid results. Simulation studies are a reliable method for assessing the power of statistical tests, however, biologists may not be familiar with simulation studies for power despite their efficacy and accessibility. Through an example of simulated Harlan Sprague-Dawley (HSD) rat organ weight data, we highlight the importance of conducting power analyses in laboratory animal research. Using simulations to determine statistical power prior to an experiment is a financially and ethically sound way to validate statistical tests and to help ensure reproducibility of findings in line with the 4R principles of animal welfare.

零假设显著性检验是实验动物研究中常用的一种统计工具。在报告实验结果时,结果的可重复性至关重要。在分析实验动物数据时,建立标准、稳健和充分的统计方法对于确保结果的可重复性和有效性至关重要。模拟研究是评估统计检验功率的一种可靠方法,然而,尽管模拟研究非常有效且易于使用,但生物学家可能并不熟悉模拟研究的功率。通过一个模拟哈兰-斯普拉格-道利(HSD)大鼠器官重量数据的例子,我们强调了在实验动物研究中进行功率分析的重要性。在实验前使用模拟来确定统计功率是一种经济上和道德上合理的验证统计测试的方法,有助于确保研究结果的可重复性,符合动物福利的 4R 原则。
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Laboratory Animals
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