合成数据生成技术对照组生存数据的比较 在瘤学临床试验中:模拟研究
Ippei Akiya1, Takuma Ishihara2, Keiichi Yamamoto3
1Biometrics, ICON Clinical Research GK, Tokyo, Japan.
JMIR medical informatics
|June 18, 2024
概括
分类和回归树 (CART) 有效地生成合成患者数据用于瘤生存率分析,优于小数据集的其他方法. 这种方法提高了合成数据在临床试验开发中的可靠性.
科学领域:
- 在瘤学瘤学.
- 临床试验开发研究
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 合成患者数据 (SPD) 的生成对于加速瘤学临床发展至关重要.
- 像CART,RF,BN和CTGAN这样的机器学习方法用于SPD生成,但它们的性能需要评估.
- 在SPD中准确反映实际患者生存数据对于可靠的分析至关重要.
研究的目的:
- 为瘤学试验确定最合适的SPD生成方法,重点关注无进展生存率 (PFS) 和总生存率 (OS).
- 为了比较模拟和评估四种生成方法:CART,RF,BN和CTGAN.
主要方法:
- 用CART,RF,BN和CTGAN生成1000个合成数据集,用于多个临床试验数据集.
- 基于PFS和OS的中位生存时间 (MST),危险比距离 (HRD) 和Kaplan-Meier (KM) 图形分析的评估方法.
- 评估每种方法模仿真实患者生存数据的统计特性的能力.
主要成果:
- 卡特始终产生合成数据,MST属于实际数据的95%置信区间 (PFS为88.8%-98.0%,OS为60.8%-96.1%).
- 卡特显示,危险比距离 (HRD) 集中在0.9左右,这表明与实际生存功能具有很高的相似性.
- BN和CTGAN不适合小型数据集,未能准确地反映统计属性;RF表现不如CART.
结论:
- 分类和回归树 (CART) 是从小型瘤学试验数据集生成合成生存数据的最有效方法.
- 在模仿真实患者数据属性方面,CART的表现优于随机森林,贝叶斯网络和条件表式生成对抗网络.
- 未来的工作可以通过整合特征工程和其他先进技术来增强基于CART的生成.
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