数据预处理选择对行为RCT结果的影响:多元分析
1Center for Behavioural and Implementation Science, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Multivariate behavioral research
|November 4, 2025
概括
随机对照试验 (RCT) 中的数据预处理选择显著影响结果,通常比统计模型更大. 透明的报告和敏感性分析对于强大的行为科学研究至关重要.
科学领域:
- 行为科学 行为科学
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 在随机对照试验 (RCT) 中,数据预处理决策可以不成比例地影响研究结论.
- 行为科学数据通常以噪音,偏差和异常值为特征,因此预处理选择至关重要.
- 预处理对RCT结果的影响,特别是在行为研究中,需要进行彻底的调查.
研究的目的:
- 量化数据预处理管道对模拟RCT中预估治疗效果的影响.
- 为了比较预处理选择与模型规范对研究结果的影响.
- 倡导行为科学RCT中预处理步骤的透明报告和敏感性分析.
主要方法:
- 在模拟的RCT数据上进行了两次多元分析,涵盖了180个分析途径.
- 分析跨越了36个预处理管道 (不同的异常处理,归算和转换) 与五个模型规格.
- 模拟使用了线性回归家族和先进的算法,如通用添加模型,随机森林和梯度增强.
主要成果:
- 预处理决策解释了估计治疗效果的绝大多数差异 (76.9%在线性模型中, 99.8%在高级算法中).
- 模型规范对方差的影响很小 (线性模型的7.5%,高级算法的0.1%).
- 特定的预处理管道极大地改变了影响估计,将其缩小了90%以上或将其膨胀了数量级.
结论:
- 数据预处理选择对行为科学中的RCT发现的影响远大于统计模型选择.
- 精心报告预处理步骤对于确保研究的稳定性和可复制性至关重要.
- 建议进行常规的灵敏度或多层次分析,以使预处理选择的影响变得透明.
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