Longitudinal designs can fortify causal inquiries of a focal predictor (i.e., treatment) on an outcome. But valid causal inferences are complicated by causal feedback between confounders and treatment over time. G-estimation of a structural nested mean model (SNMM) is designed to handle the complexities beset by measured time-varying or treatment-dependent confounding in longitudinal data. But valid inference requires correctly specifying the functional form of the SNMM, such as how the effects stay constant or change over time. In this article, we develop a g-estimation strategy for linear structural nested mean models whose causal parameters adopt the form of time-varying coefficient functions. These time-varying coefficient functions are smooth semiparametric functions of time that permit probing how the treatment effects may change curvilinearly. Further effect modification by time-invariant and time-varying covariates can be readily postulated in the SNMM to test fine-grained effect heterogeneity. We then describe a g-estimation strategy for estimating such an SNMM. We utilize the established time-varying effect model (TVEM) approach from the prevention and psychotherapy research literature for modeling flexible changes in covariate-outcome associations over time. Moreover, we exploit a known benefit of g-estimation over routine regression methods: its double robustness conferring protection against biases induced by certain forms of model misspecification. We encourage psychology researchers seeking correct causal conclusions from longitudinal data to use an SNMM with time-varying coefficient functions to assess curvilinear causal effects over time, and to use g-estimation with TVEM to resolve measured treatment-dependent confounding. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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估计曲线时变治疗效果：将结构嵌套均值模型的 g 估计与时变效果模型相结合，进行纵向因果推断。


纵向设计可以加强对结果的焦点预测因素（即治疗）的因果调查。但随着时间的推移，混杂因素和治疗之间的因果反馈使有效的因果推论变得复杂。结构嵌套均值模型 (SNMM) 的 G 估计旨在处理纵向数据中测量的时变或治疗相关混杂所带来的复杂性。但有效的推理需要正确指定 SNMM 的函数形式，例如效果如何保持恒定或随时间变化。在本文中，我们为线性结构嵌套均值模型开发了一种 g 估计策略，其因果参数采用时变系数函数的形式。这些时变系数函数是平滑的半参数时间函数，可以探究治疗效果如何曲线变化。可以在 SNMM 中轻松假设由时不变和时变协变量进行的进一步效应修改，以测试细粒度效应异质性。然后我们描述了用于估计此类 SNMM 的 g 估计策略。我们利用预防和心理治疗研究文献中已建立的时变效应模型（TVEM）方法来对协变量结果关联随时间的灵活变化进行建模。此外，我们利用了 g 估计相对于常规回归方法的一个已知优势：其双重稳健性可以防止某些形式的模型错误指定引起的偏差。我们鼓励心理学研究人员从纵向数据中寻求正确的因果结论，使用具有时变系数函数的 SNMM 来评估随时间变化的曲线因果效应，并使用 TVEM 的 g 估计来解决测量的治疗依赖性混杂因素。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。