Falkenström Fredrik, Solomonov Nili, Rubel Julian
Department of Behavioural Sciences and Learning, Linköping University, Sweden.
Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medical College, USA.
Couns Psychother Res. 2020 Sep;20(3):435-441. doi: 10.1002/capr.12293. Epub 2020 Jan 26.
The introduction of novel methodologies in the past decade has advanced research on mechanisms of change in observational studies. Time-lagged panel models allow to track session-by-session changes and focus on within-patient associations between predictors and outcomes. This shift is crucial, as change in mechanisms inherently takes place at a within-patient level. These models also enable preliminary casual inferences, which can guide the development of effective personalized interventions that target mechanisms of change, used at specific treatment phases for optimal effect. Given their complexity, panel models need to be implemented with caution, as different modeling choices can significantly affect results and reduce replicability. We outline three central methodological recommendations for use of time-lagged panel analysis to study mechanisms of change: a) Taking patient-specific effects into account, separating out stable between-person differences from within-person fluctuations over time; b) properly controlling for autoregressive effects; c) considering long-term time-trends. We demonstrate these recommendations in an applied example examining the session-by-session alliance-outcome association in a naturalistic psychotherapy study. We present limitations of time-lagged panel analysis and future directions.
在过去十年中，新方法的引入推动了观察性研究中变化机制的研究。时间滞后面板模型能够逐 session 追踪变化，并聚焦于预测因素与结果之间的患者内关联。这一转变至关重要，因为机制的变化本质上发生在患者内层面。这些模型还能够进行初步的因果推断，这可以指导针对变化机制的有效个性化干预措施的开发，这些干预措施在特定治疗阶段使用以达到最佳效果。鉴于其复杂性，面板模型的实施需要谨慎，因为不同的建模选择可能会显著影响结果并降低可重复性。我们概述了使用时间滞后面板分析来研究变化机制的三个核心方法学建议：a）考虑患者特定效应，将稳定的个体间差异与个体内随时间的波动区分开来；b）适当控制自回归效应；c）考虑长期时间趋势。我们在一个应用示例中展示了这些建议，该示例考察了一项自然主义心理治疗研究中逐 session 的联盟 - 结果关联。我们还介绍了时间滞后面板分析的局限性和未来方向。
