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Background <p>Conventional evaluations of psychological treatment effects have primarily focused on changes in average symptom levels, thereby overlooking intra-individual variability. However, evidence from intensive longitudinal data demonstrates that psychological states fluctuate dynamically over time, suggesting that treatment effects may manifest not only in mean levels but also in temporal instability. It is imperative to acknowledge the impact of variability on treatment efficacy, particularly in the context of clinical intervention research. Failure to account for this variability can lead to the misinterpretation of treatment outcomes, potentially compromising the accuracy of research findings.</p> Methods <p>The proposed methodology, under a randomized controlled trial design, is intended to facilitate the dynamic assessment of clinical treatment. The present method is based on a dynamic structural equation model framework, which allows individual-specific residual variances to be contingent on treatment allocation, thereby facilitating the direct evaluation of the impact of treatment on psychological instability. The proposed framework is illustrated with an empirical case, and the performance of dynamic treatment assessment parameters is systematically evaluated under different conditions using Monte Carlo simulations.</p> Results <p>Empirical evidence has demonstrated the efficacy of the dynamic structural equation model in capturing variations in dynamic treatment effects between experimental and control groups. The simulation results further indicate that this method provides accurate parameter recovery and stable estimates under various conditions, thereby supporting its robustness and flexibility for simulating dynamic treatment effects in intensive longitudinal data settings.</p> Conclusions <p>The present study proposes a novel perspective on dynamic treatment evaluation based on a dynamic structural equation model framework. By conceptualizing residual variance as a dynamic outcome, this framework facilitates a more nuanced comprehension of clinical change and offers a significant extension to conventional treatment-evaluation methods in randomized controlled trials.</p>

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A dynamic perspective on clinical treatment evaluation using dynamic structural equation modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Shuncheng He,
  • Wooyeol Lee

摘要

Background

Conventional evaluations of psychological treatment effects have primarily focused on changes in average symptom levels, thereby overlooking intra-individual variability. However, evidence from intensive longitudinal data demonstrates that psychological states fluctuate dynamically over time, suggesting that treatment effects may manifest not only in mean levels but also in temporal instability. It is imperative to acknowledge the impact of variability on treatment efficacy, particularly in the context of clinical intervention research. Failure to account for this variability can lead to the misinterpretation of treatment outcomes, potentially compromising the accuracy of research findings.

Methods

The proposed methodology, under a randomized controlled trial design, is intended to facilitate the dynamic assessment of clinical treatment. The present method is based on a dynamic structural equation model framework, which allows individual-specific residual variances to be contingent on treatment allocation, thereby facilitating the direct evaluation of the impact of treatment on psychological instability. The proposed framework is illustrated with an empirical case, and the performance of dynamic treatment assessment parameters is systematically evaluated under different conditions using Monte Carlo simulations.

Results

Empirical evidence has demonstrated the efficacy of the dynamic structural equation model in capturing variations in dynamic treatment effects between experimental and control groups. The simulation results further indicate that this method provides accurate parameter recovery and stable estimates under various conditions, thereby supporting its robustness and flexibility for simulating dynamic treatment effects in intensive longitudinal data settings.

Conclusions

The present study proposes a novel perspective on dynamic treatment evaluation based on a dynamic structural equation model framework. By conceptualizing residual variance as a dynamic outcome, this framework facilitates a more nuanced comprehension of clinical change and offers a significant extension to conventional treatment-evaluation methods in randomized controlled trials.