ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2024
Significant individual change should be used as a lower bound for anchor based estimates of meaningful change on patient-reported outcome scores.
Article in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Coefficients of Repeatability for Likely Change: Comparison between PROMIS Computer Adaptive Tests and Short Forms.Advances in patient-reported outcomes · 2026Article
- Internal consistency, construct validity, and responsiveness of the MRC Prion Disease Rating Scale.Journal of patient-reported outcomes · 2025Article
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3 authors.
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Abstract
Interpretation of patient-reported outcome (PRO) scores has been supported by identifying score thresholds or ranges that indicate clinical importance. There has been a recent focus on the estimation of meaningful within patient change (MWPC). While much attention has been focused on anchor-based methods, some researchers prefer that a lower bound to these estimates should exceed a change score that could be observed due to measurement error alone as a safeguard against misclassifying individual patients as changed when they have not. The standard error of measurement (SEM) is often used as the lower bound of anchor estimates. Here, we argue that the SEM is not an the best lower bound for MWPCs. Instead, statistically significant individual change as calculated by the reliable change index (RCI) should be used as the lower bound. Our argument is based on two points. First, conceptually, the SEM does not provide specific enough information to serve as a lower bound for MWPCs, which should be based on the level of observed score change that is unlikely to be due to chance alone. Second, the SEM is not appropriate for direct application to observed scores, and requires a multiplier when examining observed change instead of true change. We conclude with recommendations for using the RCI with a thoughtful range of p-values in combination with anchor estimates.
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