Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, USA.
Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA.
Stat Med. 2021 May 30;40(12):2800-2820. doi: 10.1002/sim.8929. Epub 2021 Mar 9.
This paper demonstrates the utility of latent classes in evaluating the effect of an intervention on an outcome through multiple indicators of mediation. These indicators are observed intermediate variables that identify an underlying latent class mediator, with each class representing a different mediating pathway. The use of a latent class mediator allows us to avoid modeling the complex interactions between the multiple indicators and ensures the decomposition of the total mediating effects into additive effects from individual mediating pathways, a desirable feature for evaluating multiple indicators of mediation. This method is suitable when the goal is to estimate the total mediating effects that can be decomposed into the additive effects of distinct mediating pathways. Each indicator may be involved in multiple mediation pathways and at the same time multiple indicators may contribute to a single mediating pathway. The relative importance of each pathway may vary across subjects. We applied this method to the analysis of the first 6 months of data from a 2-year clustered randomized trial for adults in their first episode of schizophrenia. Four indicators of mediation are considered: individual resiliency training; family psychoeducation; supported education and employment; and a structural assessment for medication. The improvement in symptoms was found to be mediated by the latent class mediator derived from these four service indicators. Simulation studies were conducted to assess the performance of the proposed model and showed that the simultaneous estimation through the maximum likelihood yielded little bias when the entropy of the indicators was high.
本文展示了潜在类别在通过多个中介指标评估干预对结果的影响方面的效用。这些指标是观察到的中间变量，用于识别潜在的类别中介变量，每个类别代表不同的中介途径。使用潜在类别中介变量可以避免对多个指标之间的复杂交互进行建模，并确保将总中介效应分解为来自个体中介途径的加性效应，这是评估多个中介指标的理想特征。当目标是估计可以分解为不同中介途径的加性效应的总中介效应时，这种方法是合适的。每个指标可能涉及多个中介途径，同时多个指标可能对单个中介途径有贡献。每个途径的相对重要性可能因个体而异。我们将这种方法应用于一项为期 2 年的聚类随机试验中成人首发精神分裂症患者前 6 个月数据的分析。考虑了四个中介指标：个体复原力训练、家庭心理教育、支持性教育和就业以及药物治疗的结构性评估。研究发现，这些服务指标所衍生的潜在类别中介变量可以介导症状的改善。进行了模拟研究来评估所提出模型的性能，结果表明当指标的熵值较高时，通过最大似然同时进行估计几乎没有偏差。
