纵向研究中时变治疗效果的估计。

IF 7.8 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Psychological methods Pub Date : 2025-04-01 Epub Date: 2023-05-11 DOI:10.1037/met0000574
Wen Wei Loh, Dongning Ren
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引用次数: 0

摘要

纵向研究设计经常用于调查自然观察到的预测因子(治疗)随时间对结果的影响。由于治疗在每个时间点或波不是随机分配的,其因果效应的有效推断需要调整混淆每个治疗-结果关联的协变量。但是,调整不可避免地时变的协变量是充满困难的。一方面,对受治疗影响的变量进行标准回归调整会导致严重的偏倚。另一方面,从混淆调整中忽略时变协变量会产生虚假的关联,从而导致严重的偏差。因此,包括或省略时变协变量进行混杂调整都可能导致不正确的推断。在本文中,我们从因果推理文献中引入了一种估计策略,用于在时变混杂存在的情况下评估时变处理的因果效应。在特定波处处理效果的g估计是通过仔细调整所有变量的预处理实例而忽略任何后处理实例来进行的。所介绍的方法具有各种吸引人的特点。时变协变量的效应修正可以用协变量-处理相互作用来研究。在允许的任何平均模型下,处理可以是连续的或不连续的。无偏估计需要正确地指定治疗或结果的平均模型,但不一定两者都有。治疗和结果模型可以用标准回归函数拟合。总之,g估计是有效的、灵活的、健壮的,并且相对容易实现。(PsycInfo Database Record (c) 2025 APA,版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating time-varying treatment effects in longitudinal studies.

Longitudinal study designs are frequently used to investigate the effects of a naturally observed predictor (treatment) on an outcome over time. Because the treatment at each time point or wave is not randomly assigned, valid inferences of its causal effects require adjusting for covariates that confound each treatment-outcome association. But adjusting for covariates which are inevitably time-varying is fraught with difficulties. On the one hand, standard regression adjustment for variables affected by treatment can lead to severe bias. On the other hand, omitting time-varying covariates from confounding adjustment precipitates spurious associations that can lead to severe bias. Thus, either including or omitting time-varying covariates for confounding adjustment can lead to incorrect inferences. In this article, we introduce an estimation strategy from the causal inference literature for evaluating the causal effects of time-varying treatments in the presence of time-varying confounding. G-estimation of the treatment effect at a particular wave proceeds by carefully adjusting for only pre-treatment instances of all variables while dispensing with any post-treatment instances. The introduced approach has various appealing features. Effect modification by time-varying covariates can be investigated using covariate-treatment interactions. Treatment may be either continuous or noncontinuous with any mean model permitted. Unbiased estimation requires correctly specifying a mean model for either the treatment or the outcome, but not necessarily both. The treatment and outcome models can be fitted with standard regression functions. In summary, g-estimation is effective, flexible, robust, and relatively straightforward to implement. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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