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Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes.

Pascal Woaye-Hune1,2, Jean-Benoit Hardouin1,3, Paul-Antoine Lehur4

  • 1Inserm, Université Bretagne-Loire - Université de Nantes - Université de Tours, UMR U1246 SPHERE "Methods in patient-centered outcomes and health research", Nantes, France.

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Summary

Estimating the Minimal Clinically Important Difference (MCID) for patient-reported outcomes is sensitive to missing data and multiple measurements. Methodological choices significantly impact MCID values, requiring careful consideration in research.

Keywords:
Longitudinal modelingMethodologyMinimal clinically important differenceMinimal important differenceMissing dataPatient-reported outcomes

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Area of Science:

  • Health Outcomes Research
  • Clinical Trial Methodology
  • Psychometrics

Background:

  • Methodological issues in estimating the Minimal Clinically Important Difference (MCID) for Patient-Reported Outcomes (PROs) instruments are often overlooked.
  • Inappropriate handling of missing data and the use of multiple time points can introduce bias and reduce precision in MCID estimation.
  • These critical issues require careful attention in the context of MCID estimation.

Purpose of the Study:

  • To highlight major methodological issues in MCID estimation using a real dataset.
  • To assess the impact of missing data management and multiple measurement occasions on MCID.
  • To provide practical insights into robust MCID estimation for PROs.

Main Methods:

  • Utilized data from the LIGALGO study (a French Randomized Controlled Trial).
  • Estimated MCID for the SF-36 General Health score using distribution-based and anchor-based methods.
  • Applied various missing data imputation techniques and longitudinal modeling to account for multiple measurement occasions and baseline dependence.

Main Results:

  • Distribution-based MCID estimates showed high variability (3–26 points), with only 0.2 SD and 1/3 SD methods aligning with anchor-based results (4–7 points).
  • Missing data imputation techniques significantly impacted MCID estimates; simple imputation yielded out-of-range values, and multiple imputations potentially underestimated MCID.
  • Using three measurement occasions increased precision but reduced MCID estimates for improvement.

Conclusions:

  • This study demonstrates the substantial impact of commonly overlooked methodological issues on MCID estimation.
  • Simulation studies are recommended to further investigate these methodological challenges in MCID estimation.
  • Emphasizes the need for cautious consideration of data handling and measurement frequency in PRO research.