估计曲线时间变化的治疗效应:将结构性嵌套平均值模型的g估计与时间变化的效应模型相结合,用于纵向因果推断
1Department of Quantitative Theory and Methods, Emory University.
Psychological methods
|February 15, 2024
概括
本研究引入了结构嵌套平均模型 (SNMMs) 的新g估计策略,以准确评估纵向数据中的时间变化的治疗效应. 该方法有助于解决复杂的混,在心理学研究中获得更可靠的因果推断.
科学领域:
- 因果推理因果推理
- 纵向数据分析 纵向数据分析
- 半参数建模 半参数建模
背景情况:
- 纵向研究对于因果推断至关重要,但面临着因时间变化的混而带来的挑战.
- 标准方法在时间的推移中与混因子和治疗之间的复杂反循环作斗争.
- 准确的因果结论需要对纵向数据进行可靠的统计模型.
研究的目的:
- 为具有时间变化的系数函数的结构嵌套平均模型 (SNMMs) 开发一个g估计策略.
- 为了能够评估随时间变化的曲线治疗效应.
- 为心理学研究人员提供一种方法,以便从纵向数据中得出更有效的因果结论.
主要方法:
- 开发了对具有时间变化的系数函数的线性SNMMs的g估计策略.
- 利用时间变化效应模型 (TVEM) 来灵活建模共变量-结果关联.
- 利用g估计的双重稳定性来抵御模型的错误规范.
主要成果:
- 拟议的方法允许平滑,半参数估计时间变化的因果参数.
- 通过共变量对效应进行修改,可用于测试细粒度异质性.
- G-估计提供了防止某些模型错误规范带来的偏差的保护.
结论:
- 具有时间变化的系数的SNMMs的g估计策略对于分析纵向数据是有效的.
- 这种方法提高了探测治疗效果如何随着时间的推移而变化的能力.
- 鼓励研究人员使用具有g估计和TVEM的SNMM来解决治疗依赖的混问题.
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