General psychiatry · 2026
Enhancing clinical interpretation of patient-reported outcomes: A cross-platform toolkit for clinically important difference estimation using R Shiny and statistical analysis system.
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Qin Zongshi, Yu Yongpei, Liao Kuan, Shi Dongdong, Wang Zhen, Gu Hongqiu, Wu Jiani
Аннотация
OBJECTIVE: Estimating clinically important differences (CIDs) for patient-reported outcome measures (PROMs) is essential for translating statistical findings into clinically meaningful treatment effects. We developed estimating clinically important differences (ESTICID), a cross-platform toolkit implemented in both R Shiny and statistical analysis system, to estimate and visualise CIDs in longitudinal studies.
METHODS: ESTICID applies an anchor-based approach using linear mixed-effects models with random intercepts. The framework accommodates repeated measures, supports both continuous and categorical anchors and handles incomplete follow-up data under a missing-at-random assumption. A multicenter randomised controlled trial dataset was used for illustration.
EXAMPLES: Using the illustrative dataset, we show that ESTICID generates clinically interpretable CID estimates across different levels of improvement and provides graphical outputs that facilitate interpretation. In simulation analyses, the estimates remained stable under increasing levels of missing follow-up data. The R Shiny implementation further enables interactive, browser-based use without programming.
CONCLUSIONS: ESTICID provides a practical and accessible framework for anchor-based CID estimation in longitudinal PROM research. By integrating methodological rigour with cross-platform implementation, it may improve the interpretation of treatment effects, responder definitions, sample size estimation and evidence-to-decision processes in psychiatry and related clinical fields.
METHODS: ESTICID applies an anchor-based approach using linear mixed-effects models with random intercepts. The framework accommodates repeated measures, supports both continuous and categorical anchors and handles incomplete follow-up data under a missing-at-random assumption. A multicenter randomised controlled trial dataset was used for illustration.
EXAMPLES: Using the illustrative dataset, we show that ESTICID generates clinically interpretable CID estimates across different levels of improvement and provides graphical outputs that facilitate interpretation. In simulation analyses, the estimates remained stable under increasing levels of missing follow-up data. The R Shiny implementation further enables interactive, browser-based use without programming.
CONCLUSIONS: ESTICID provides a practical and accessible framework for anchor-based CID estimation in longitudinal PROM research. By integrating methodological rigour with cross-platform implementation, it may improve the interpretation of treatment effects, responder definitions, sample size estimation and evidence-to-decision processes in psychiatry and related clinical fields.