评估瘤学试验结果对真实世界患者的概括性,使用基于机器学习的试验模拟
Xavier Orcutt1, Kan Chen2, Ronac Mamtani3,4
1Navajo Indian Health Service, Chinle, AZ, USA.
Nature medicine
|January 3, 2025
概括
对癌症药物的随机对照试验 (RCT) 通常不适用于所有患者. 与临床试验结果相比,在现实环境中的高风险患者从治疗中获得的益处较小.
科学领域:
- 在瘤学瘤学.
- 临床试验 临床试验
- 医疗信息学 医疗信息学
背景情况:
- 瘤学中的随机对照试验 (RCT) 往往对多样化,现实世界患者群体的概括性有限.
- 已知RCT中的限制性资格标准有助于解决这个问题,但基于预后风险的选择偏差的影响尚未完全理解.
研究的目的:
- 开发和验证一个框架,TrialTranslator,用于系统地评估瘤学RCT对现实患者的概括性.
- 调查预后风险异质性在RCT与现实世界瘤学数据之间的概括性差距中的作用.
主要方法:
- 利用全国范围的电子健康记录数据库 (Flatiron Health) 模拟了四种流行先进的固体恶性瘤的11个里程碑式的RCT.
- 采用机器学习模型将患者分为低风险,中风险和高风险预后表型.
- 在RCT群体和现实患者表型之间比较生存时间和与治疗相关的生存益处.
主要成果:
- 低风险和中等风险表型患者的生存结果和治疗益处与原始RCT中的患者相似.
- 与RCT发现相比,高风险的预后表型表现出明显减少的生存时间和与治疗相关的生存益处.
- 稳定性评估,包括子组分析和数据模拟,证实了这些发现.
结论:
- 现实世界瘤病人的预后异质性显著导致RCT结果有限的概括性.
- 机器学习框架显示了增强个体患者决策支持和估计现实世界治疗益处的前景,可能为未来的临床试验设计提供信息.
相关概念视频
01:21Cancer Survival Analysis
328
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
328
02:43Mouse Models of Cancer Study
5.5K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
5.5K