统计模拟表明,科学家不需要增加总体样本大小作为默认值,当包括两性在体内研究中的体内研究
Benjamin Phillips1, Timo N Haschler2, Natasha A Karp1
1Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom.
PLoS biology
|June 8, 2023
概括
在使用适当的因子分析时,将两性纳入研究研究并不会降低统计能力. 这种方法提高了对基于性别的生物差异和药物开发的理解,超过了轻微的权力考虑.
科学领域:
- 生命科学 生命科学
- 生物医学研究生物医学研究
- 动物模型 动物模型
背景情况:
- 在基本生物学和药物开发的体内研究中,越来越重视性别表示.
- 资助机构和期刊的授权促进性别包容,但常规实施面临障碍.
- 值得关注的是,对于统计能力所需的样本大小的感知增加,这引发了伦理和资源问题.
研究的目的:
- 调查包括两性在研究研究中的统计能力的影响.
- 评估不同数据分析策略对检测治疗效应的影响.
- 为了解决关于样本大小和统计敏感性的误解,在纳入男性和女性受试者时.
主要方法:
- 使用不同性别差异和治疗效果的人工数据集进行模拟.
- 使用适当的因子分析 (例如,双向ANOVA) 和不适当的方法 (聚合/分类后的t测试) 分析数据.
- 在各种模拟场景中评估统计功率,包括基线性别差异和性别依赖的治疗效应.
主要成果:
- 在将样本大小按性别划分并采用因数分析时,没有观察到显著的统计能力损失.
- 不恰当的分析方法 (t-测试) 导致了统计能力的损失.
- 了解性别特异性影响的好处通常超过了电力考虑,即使在电力减少的罕见场景中也是如此.
结论:
- 建议使用因子分析分析两性数据,以保持统计能力.
- 将样本大小分为男性和女性受试者应该是研究设计中的标准策略.
- 准确的数据分析对于最大限度地提高性别包容性研究的实用性和促进生物理解至关重要.
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