疾病风险评分方法的比较,以研究治疗效果异质性:一个模拟研究
Haedi E Thelen1,2, Wei Yang1, Sean Hennessy1,2
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
American journal of epidemiology
|March 6, 2026
概括
在随机试验中,用于导出疾病风险得分 (DRS) 的分割样本方法在估计治疗效果方面显示出较少的偏差,而不是仅对照或全样本方法. 当有足够的数据可用时,这种方法是可取的.
科学领域:
- 生物统计学 生物统计学
- 临床试验 临床试验
- 流行病学 流行病学
背景情况:
- 使用疾病风险得分 (DRS) 估计治疗效果异质性在随机试验中至关重要.
- 当外部模型不可用时,DRS模型的最佳内部导出方法是不确定的.
- 评估不同风险层的治疗效应有助于了解治疗变异性.
研究的目的:
- 用三种内部DRS模型推导方法,在DRS定义的层中比较估计治疗效应的偏差.
- 评估内部DRS模型装配的仅控制,全样本和分割样本方法的性能.
- 确定在随机试验中通过DRS估计治疗效果异质性的最有效方法.
主要方法:
- 进行了一项模拟研究,比较DRS模型的三个内部导出方法.
- 方法包括将DRS模型仅适用于控制器,全样本和随机分割样本的控制器.
- 模拟对治疗效应 (odds比率),治疗-共变相互作用,结果发生率,样本大小,随机化比率和DRSc统计数据进行了变化.
主要成果:
- 分样方法在估计的治疗效果中显示了最低的整体百分比偏差 (OPB) (OR=0.8与相互作用的7.7%).
- 只有对照 (OPB=15.6%) 和全样本 (OPB=22.1%) 的方法显示出明显更高的偏差.
- 使用分割样本方法减少偏见在更大的样本大小,更高的结果发生率,更高的治疗对照比率和更大的c统计数据中更为明显.
结论:
- 在随机试验中,分割样本方法似乎是DRS估计治疗效果异质性的首选方法.
- 当有足够的数据支持稳定的预测模型开发时,这种方法特别有利.
- 这些发现为为临床试验分析选择最佳的内部DRS导出策略提供了指导.
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