边缘结构模型与治疗轨迹的潜在类增长分析:老年人中用于初级预防的他类药物
Awa Diop1,2, Caroline Sirois2,3, Jason Robert Guertin1,2,4
1Departement de medecine sociale et preventive, Universite Laval, Quebec, Canada.
Statistical methods in medical research
|September 26, 2023
概括
潜在类增长分析与边缘结构模型相结合,可以准确评估药物坚持模式及其对健康结果的影响. 这种新的方法最大限度地减少了纵向研究中的偏差,改善了心血管事件风险预测.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 隐性类增长分析 (LCGA) 用于基于纵向数据对个体进行分组,但其与标准模型的结合可能会引入偏差.
- 纵向研究中的时间变化的共变量可以充当混者和调解者,使因果推理复杂化.
- 传统方法在分析复杂的治疗轨迹时,难以充分解决混偏见.
研究的目的:
- 提出一种新的统计方法,将LCGA与边缘结构模型 (MSM) 结合起来,以解决纵向研究中的混偏差.
- 使用LCGA准确地将个体分为不同的药物坚持模式.
- 通过使用MSM来估计这些坚持轨迹对健康结果的因果关系.
主要方法:
- 隐性类增长分析 (LCGA) 用于识别不同的药物坚持模式.
- 选择了一个有效的边际结构模型 (MSM) 来将坚持轨迹与结果联系起来.
- 感兴趣的参数被定义为真正的MSM在工作模型上的投影.
- 模拟研究将拟议的方法与未调整的,基线调整的,随时间变化的协变量调整的,以及轨迹组加权 (IPTW) 的逆概率模型进行了比较.
- 这种方法应用于65岁以上的加拿大大大群开始服用他类药物的人群.
主要成果:
- 拟议的LCGA和MSM方法在模拟研究中显示出极少或没有偏见.
- 在模拟中实现了适当的置信区间覆盖,表明可靠的估计.
- 该方法成功地应用于52,790名他类药物使用者的现实数据集.
结论:
- 结合LCGA和MSM方法有效地解决了纵向治疗分析中的混偏差.
- 该方法为了解药物坚持轨迹对健康结果的影响提供了一个强大的框架.
- 这些发现有助于改善观察性健康研究中的因果推断,特别是在慢性疾病管理方面.
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