Benefits of a factorial design focusing on inclusion of female and male animals in one experiment
Benefits of a factorial design focusing on inclusion of female and male animals in one experiment
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DOI：
10.1007/s00109-019-01774-0
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发表时间：
2019-06-01
影响因子：
4.7
通讯作者：
Tresch, Achim
中科院分区：
文献类型：
--
作者：
Buch, Thorsten;Moos, Katharina;Tresch, Achim
疾病的发生、临床表现和结果在男性和女性之间存在差异。然而，大多数情况下，女性和男性的待遇是相似的，这通常是基于过度代表一种性别的实验数据。对生物医学研究中持续存在的性别偏见的解释是一种误解，即对性别特异性效应的分析将使样本量和成本加倍。我们设计了一项分析，在包括雄性和雌性动物的研究背景下，测试析因研究设计的潜在益处。我们选择2x2因子设计方法来研究治疗、性别的影响，以及在假设情况下治疗和性别的相互作用项。我们计算了在不同的实验设置下，以足够的功率检测给定量级的效应所需的样本量。我们证明，在不测试性别影响的情况下，在实验设置中包括两性，在我们的场景中不需要或很少需要额外的动物。这些实验设计仍然允许以低成本探索性别效应。在验证性设计而不是探索性设计中，我们观察到总样本量最多增加33%。由于与此数学模型相关的复杂性需要统计学专业知识，因此我们生成并提供了一个样本量计算器，用于规划析因设计实验。对于包含性别的研究，建议采用析因设计，并且无需过多的额外工作即可进行性别特异性分析。我们易于使用的计算工具为设计两性研究提供了帮助，并解决了目前临床前研究中的性别偏见。关键信息中心点两性都应该纳入动物研究。性别效应的探索性研究可以在动物数量没有或很少增加的情况下进行。性别效应的验证性分析每次研究最多需要33%以上的动物。我们的计算工具支持男女研究的设计。
Disease occurrence, clinical manifestations, and outcomes differ between men and women. Yet, women and men are most of the time treated similarly, which is often based on experimental data over-representing one sex. Accounting for persisting sex bias in biomedical research is the misconception that the analysis of sex-specific effects would double sample size and costs. We designed an analysis to test the potential benefits of a factorial study design in the context of a study including male and female animals. We chose a 2x2 factorial design approach to study the effect of treatment, sex, and an interaction term of treatment and sex in a hypothetical situation. We calculated the sample sizes required to detect an effect of a given magnitude with sufficient power and under different experimental setups. We demonstrated that the inclusion of both sexes in experimental setups, without testing for sex effects, requires no or few additional animals in our scenarios. These experimental designs still allow for the exploration of sex effects at low cost. In a confirmatory instead of an exploratory design, we observed an increase in total sample sizes by 33%, at most. Since the complexities associated with this mathematical model require statistical expertise, we generated and provide a sample size calculator for planning factorial design experiments. For the inclusion of sex, a factorial design is advisable, and a sex-specific analysis can be performed without excessive additional effort. Our easy-to-use calculation tool provides help in designing studies with both sexes and addresses the current sex bias in preclinical studies.Key messages center dot Both sexes should be included into animal studies.center dot Exploratory study of sex effects can be conducted with no or small increase in animal number.center dot Confirmatory analysis of sex effects requires maximum 33% more animals per study.center dot Our calculation tool supports the design of studies with both sexes.