在随机控制试验环境中生成现实的合成表格数据的框架
Niki Z Petrakos1, Erica E M Moodie1, Nicolas Savy2
1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Québec, Canada.
Statistics in medicine
|August 13, 2025
概括
为健康研究,特别是随机对照试验 (RCT) 生成现实的合成表格数据具有挑战性. 使用R-葡萄和回归模型的顺序方法最好保存数据分布,以获得准确的合成RCT数据.
科学领域:
- 医疗信息学 医疗信息学
- 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:
- 现实的合成数据生成对健康研究至关重要,有助于数据共享和隐私保护.
- 生成复杂的合成表格数据,特别是随机对照试验 (RCT),仍然是一个重大挑战.
- 目前的方法缺乏关于对合成表式RCT数据保持多变量数据分布的共识.
研究的目的:
- 为了比较策略和技术,生成现实的合成表格数据随机对照试验 (RCTs).
- 确定最有效的方法,以保护合成RCT数据集中的基础数据分布.
- 解决流行病学和临床研究中可靠合成数据的需求.
主要方法:
- 几种数据生成策略和三种技术 (两个机器学习,一个统计) 的实证比较.
- 使用R-葡萄模型来生成基线变量.
- 用于治疗后分配变量的回归模型,模仿RCT结果.
主要成果:
- 顺序生成方法在创建合成表格RCT数据方面被证明是最有效的.
- 通过R-vine copula模型,成功生成了现实的基线变量.
- 随后的回归模型准确地捕获了治疗后分配变量的特征,包括试验结果.
结论:
- 拟议的顺序生成策略,结合R-vine copula和回归模型,是生成合成表格RCT数据的最佳方法.
- 这种方法有效地保留了现实的数据特征和多变量分布.
- 这些发现提供了一个强大的解决方案,用于为临床试验创建保护隐私的合成数据.
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