贝叶斯的方法设计复制研究的设计方法
Samuel Pawel1, Guido Consonni2, Leonhard Held1
1Department of Biostatistics, Center for Reproducible Science, University of Zurich.
Psychological methods
|August 10, 2023
概括
贝叶斯方法通过优化样本大小来增强复制研究,将原始数据与外部知识相结合,以进行准确的预测. 这确保了足够的成功概率,导致了更具信息性和成本效益的研究设计.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 研究方法研究方法研究方法学
背景情况:
- 复制研究对于验证科学主张至关重要.
- 确定适合复制的样本大小是具有挑战性的,平衡结论性与资源效率.
- 现有的方法可能无法完全考虑参数不确定性.
研究的目的:
- 证明贝叶斯方法在复制研究中的样本大小确定中的实用性.
- 为设计具有信息性和成本效益的复制研究提供框架.
- 整合原始数据和外部知识,以便进行可靠的样本规模规划.
主要方法:
- 利用贝叶斯框架将先前信息和原始数据结合起来.
- 开发了基于设计前分布的复制数据预测方法.
- 在正常-正常等级模型中调查样本大小的确定.
主要成果:
- 贝叶斯式方法使得样本大小的选择能够确保复制成功的高概率.
- 该框架适应了各种贝叶斯和非贝叶斯的标准来定义复制成功.
- 分析结果可用于正常-正常等级模型,传统方法作为特殊情况.
结论:
- 贝叶斯样本大小的确定为复制研究提供了更强大和更有信息的方法.
- 通过R包BayesRepDesign提供的拟议方法,促进了具有成本效益的研究设计.
- 这种方法提高了科学复制过程的可信度和效率.
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