代理终点 - 将临床试验和真实世界数据相结合
Maxim Kryukov1, Kathleen P Moriarty2, Macarena Villamea3
1Data & Computational Science, R&D, Sanofi, Barcelona, Spain.
Journal of biomedical informatics
|September 19, 2024
概括
我们开发了一种新方法,通过创建代理模型,在真实世界数据 (RWD) 中估计疾病严重程度. 这使得使用电子健康记录在更大患者群体中更好地评估药物疗效.
科学领域:
- 生物医学信息学 生物医学信息学
- 健康 数据科学 数据科学
- 临床试验分析
背景情况:
- 随机对照试验 (RCT) 使用特定疾病严重程度的终点来监测治疗效果.
- 现实世界数据 (RWD) 往往缺乏这些精确的终点,限制了其在较大人群中用于药物疗效评估的使用.
研究的目的:
- 开发一种方法来学习RWD中疾病终点的代理模型.
- 为了使RWD能够用于评估更广泛的患者群体的药物疗效.
主要方法:
- 通过使用RCT数据的多阶段学习框架开发了一种组合技术.
- 特征选择确定了RWD中存在的重要疾病驱动因素.
- 使用可解释提升机 (EBM) 来创建可解释的代理模型,捕捉非线性关系.
主要成果:
- 这种方法在类风湿性关节炎 (RA) 和亚托皮性皮肤炎 (AD) 上得到了证明.
- 综合特征选择和预测方法在两种疾病中表现强.
- 结果改进了现有的预测疾病严重程度评分的方法.
结论:
- 随着时间的推移,精确的疾病严重程度跟踪对于患者的理解和管理至关重要.
- 该框架使RA和AD的新应用成为可能,包括治疗效果估计和RWD的预后评分.
- 该方法可扩展到其他疾病,其严重程度在电子健康记录中表现不佳.
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