使用个人参与者数据元分析估计个性化治疗效应
Florie Bouvier1, Anna Chaimani2,3, Etienne Peyrot2
1Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France. florie.brion-bouvier@u-paris.fr.
BMC medical research methodology
|March 26, 2024
概括
估计个性化治疗效应 (ITE) 对个性化医学至关重要. 将个人参与者数据元分析 (IPD-MA) 与S学习者策略相结合,可以为各种结果提供更好的ITE估计.
科学领域:
- 生物统计学 生物统计学
- 个性化医疗是个性化的医疗.
- 流行病学 流行病学
背景情况:
- 识别受益于特定干预措施的个体是个性化医学的关键.
- 估计个性化治疗效应 (ITE) 往往需要大量数据集,因为单个试验不足.
- 个人参与者数据元分析 (IPD-MA) 提供了可靠ITE估计的解决方案,但将它们与预测模型相结合还未得到充分探索.
研究的目的:
- 在个人参与者数据元分析 (IPD-MA) 中,比较不同的单阶段建模方法来估计个性化治疗效果 (ITE).
- 评估各种策略和风险预测模型的性能,以使用模拟和真实世界的数据进行ITE估计.
主要方法:
- 使用S学习者和T学习者策略,比较了五个单阶段模型 (天真,随机拦截,分层拦截,排名-1,完全分层).
- 一个蒙特卡洛模拟研究评估模型性能与二进制和时间到事件的结果在各种场景下.
- 在模拟数据和INDANA IPD-MA数据集上使用c-statistic对效益,预测校准和平均平方误差来评估性能.
主要成果:
- 在模拟中,S-learner策略在对二进制和时间到事件结果的ITE估计方面表现出卓越的表现.
- 没有一个特定的风险预测模型始终显示出比其他模型更好的结果.
- 对于具有二进制结果的INDANA数据集,天真和随机拦截模型表现出最佳性能.
结论:
- 建议采用S-learner策略,包括治疗相互作用,以改善ITE估计.
- 在这种IPD-MA背景下,没有一种特定的风险预测方法在其他方法上表现出显著的优势.
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