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Enhancing clinical interpretation of patient-reported outcomes: A cross-platform toolkit for clinically important
Zongshi Qin1,2, Yongpei Yu2, Kuan Liao3
1Center for Digital Health and Artificial Intelligence Peking University First Hospital Beijing China.
General Psychiatry
|August 15, 2026
Summary
Estimating clinically important differences (CIDs) for patient-reported outcome measures (PROMs) is crucial for understanding treatment effects. The new ESTICID toolkit offers a practical, accessible framework for anchor-based CID estimation in longitudinal studies.
Area of Science:
- Clinical Research Methodology
- Biostatistics
- Psychiatry
Background:
- Clinically important differences (CIDs) are vital for interpreting patient-reported outcome measures (PROMs).
- Translating statistical significance into clinical relevance requires robust CID estimation methods.
- Longitudinal studies collecting PROMs necessitate tools for meaningful data interpretation.
Purpose of the Study:
- To develop and introduce ESTICID, a cross-platform toolkit for estimating and visualizing CIDs in longitudinal studies.
- To provide a practical and accessible framework for researchers in clinical fields.
Main Methods:
- Utilized an anchor-based approach with linear mixed-effects models and random intercepts.
- Accommodated repeated measures, continuous/categorical anchors, and missing data under a missing-at-random assumption.
- Implemented the toolkit in R Shiny and a statistical analysis system for broad accessibility.
Main Results:
- ESTICID successfully generated clinically interpretable CID estimates using an illustrative dataset.
- Graphical outputs from ESTICID facilitated data interpretation.
- Simulation analyses demonstrated the stability of CID estimates with increasing missing data.
Conclusions:
- ESTICID offers a practical and accessible framework for anchor-based CID estimation in longitudinal PROM research.
- The toolkit enhances the interpretation of treatment effects, responder definitions, and sample size estimations.
- ESTICID has the potential to improve evidence-to-decision processes in psychiatry and related clinical fields.
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