一种因果调解方法来解释治疗和相互间的事件的相互作用:使用假设策略
Kunpeng Wu1,2, Xiangliang Zhang1,2, Meng Zheng1,2
1Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Statistics in medicine
|September 5, 2024
概括
这项研究表明,考虑治疗间流行的事件 (IE) 相互作用对于临床试验中的准确估计至关重要. 与相互作用的G估计提供了公正的结果,与忽略这种相互作用的方法不同.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 药物监督 药物监督 药物监督
背景情况:
- 假设策略在临床试验中经常用于处理间流事件 (IE).
- 当前的指导方针和研究往往忽视了治疗-IE相互作用对IE处理策略的估计和影响.
研究的目的:
- 用模拟来评估三个估计器的性能,对治疗-IE相互作用进行不同的考虑.
- 为了比较这些估计器在现实世界临床试验环境中的估计准确性.
主要方法:
- 基于阿尔茨海默病试验的模拟数据被用于评估G估计偏差 (有和没有相互作用) 和IE忽略估计.
- 场景包括治疗-IE相互作用效应,主要效应的-50%至50%.
- 用于进行比较分析的是甲维持治疗的真实随机试验.
主要成果:
- 结合治疗-IE相互作用的G估计显示了所有测试场景的不偏见估计.
- 当相互作用效应存在时,忽视相互作用或IE导致显著偏差 (分别为0.093和0.241).
- 在真实试验中,没有相互作用的G估计和IE忽略的估计高估了治疗效果的33.55%和34.36%,与有相互作用的G估计相比.
结论:
- 考虑治疗-IE相互作用对于临床试验中的准确估计和定义和估计至关重要.
- 建议默认将治疗-IE相互作用纳入估计器,以确保可靠和公正的结果.
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