A Two-Step Robust Estimation Approach for Inferring Within-Person Relations in Longitudinal Design: Tutorial and Simulations

Psychological researchers have shown an interest in disaggregating within-person variability from between-person differences. This paper provides a tutorial, simulation, and illustrative example of a new approach proposed by Usami (2023). This approach consists of a two-step procedure: within-person variability scores (WPVS) for each person, which are disaggregated from the stable traits of that person, are predicted using structural equation modeling, and causal parameters are then estimated via a potential outcome approach, such as by using structural nested mean models (SNMMs). This method has several advantages: (i) the flexible inclusion of curvilinear and interaction effects for WPVS as latent variables in treatment and outcome models, (ii) more accurate estimates of causal parameters for reciprocal relations can be obtained under certain conditions owing to them being doubly robust, even if unobserved time-varying confounders and model misspecifications exist, (iii) no models for (the distributions of) observed time-varying confounders are needed for estimation, and (iv) the risk of obtaining improper solutions is reduced. Estimation performances are investigated through large-scale simulations and it shows that the proposed approach works well in many conditions if longitudinal data with T≥4 are available. An analytic example using data from the Tokyo Teen Cohort (TTC) study is also provided.心理学研究者一直致力于将个体内变异（within-person variability）与个体间差异（between-person differences）进行拆分解析。本文针对Usami（2023）提出的全新研究方法，提供了教程、模拟验证与实例演示。该方法包含两步流程：首先通过结构方程模型（structural equation modeling, SEM）对每名个体的个体内变异得分（within-person variability scores, WPVS，该得分已从个体稳定特质中拆分得到）进行预测；随后借助潜在结果方法（potential outcome approach）估计因果参数，例如使用结构嵌套均值模型（structural nested mean models, SNMMs）。该方法具备多项优势：(i) 可灵活将个体内变异得分（WPVS）的曲线效应与交互效应作为潜变量（latent variables）纳入干预与结果模型；(ii) 即便存在未观测到的时变混杂因素（time-varying confounders）与模型设定误差（model misspecifications），由于该方法具备双重稳健性（doubly robust），在特定条件下仍可获得更精准的互惠关系因果参数估计结果；(iii) 估计过程无需构建观测时变混杂因素（及其分布）的模型；(iv) 可降低得到非恰当解的风险。研究通过大规模模拟实验对估计性能进行了验证，结果表明：若可获取T≥4的纵向数据（longitudinal data），该方法在多数场景下均能表现良好。本文还提供了一项基于东京青少年队列（Tokyo Teen Cohort, TTC）研究数据的分析实例。