Lundquist and colleagues compared four commonly used methods for calculating the anchor-based minimal important change (MIC), also known as minimal clinically important difference (MCID) for the Knee injury and Osteoarthritis Outcome Score (KOOS) [1]. This study is important because multiple methods exist for calculating clinically important thresholds for group-based interpretation of KOOS changes, a common patient reported outcome measure (PROM) with no strongly endorsed gold standard. Anchor-based methods require patient input via an anchor, designed to rate the clinical importance of PROM change scores. Anchor-based methods of important change are considered by most to have the strongest clinical relevance [2]. Our concern rests with the assumption that all four methods make, that no measurement error exists in PROMs when estimating MICs/MCIDs. This assumption is incorrect, and we have addressed this problem and others in prior publications [[3], [4], [5], [6]].
Lundquist and colleagues [1] used the arithmetic subtraction method to obtain pre-post PROM differences, which then were subsequently used in all four methods for MIC/MCID estimation. Difference scores contain measurement error originating from pre and post PROM scores, as well as the difference score itself. Psychometric theories provide insight into dealing with measurement error in test scores. None of the four methods nor the confidence intervals estimated from the measurement error-prone difference scores is a remedy to effectively handle measurement error. Latent variable modeling is, in our view, the best place to start before asking the question of what is the best method to estimate MIC/MCID from the change scores and the anchor. In the absence of attempts to explicitly deal with measurement error in the PROMs, recommendations to promote a particular MIC method are, in our view, ill advised.
Handling Editor: Professor H Madry
Contributor Information
Daniel L. Riddle, Email: dlriddle@vcu.edu.
Levent Dumenci, Email: ldumenci@temple.edu.
References
- 1.Lundquist H.K., Øiestad B.E., Sexton J., Risberg M.A., Østerås N. Minimal important change of the knee injury and osteoarthritis outcome score in patients with mild to moderate knee osteoarthritis – using three different anchor-based methods. Osteoarthr Cartil Open. 2025;7 doi: 10.1016/j.ocarto.2025.100699. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.McGlothlin A.E., Lewis R.J. Minimal clinically important difference: defining what really matters to patients. JAMA, J. Am. Med. Assoc. 2014;312:1342–1343. doi: 10.1001/jama.2014.13128. [DOI] [PubMed] [Google Scholar]
- 3.Riddle D.L., Dumenci L. Limitations of minimal clinically important difference estimates and potential alternatives. J. Bone Joint Surg. 2024:931–937. doi: 10.2106/JBJS.23.00467. [DOI] [PubMed] [Google Scholar]
- 4.Riddle D.L., Dumenci L. Commentary on finding meaning in patient-reported outcome change scores: a seemingly unquenchable thirst for understanding. Osteoarthr. Cartil. 2022;30:768–771. doi: 10.1016/J.JOCA.2022.03.006. [DOI] [PubMed] [Google Scholar]
- 5.Riddle D.L., Dumenci L. Letter to the editor: there are considerable inconsistencies among minimum clinically important differences in TKA: a systematic review. Clin. Orthop. Relat. Res. 2023;481 doi: 10.1097/CORR.0000000000002591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Riddle D.L., Dumenci L. Performance of baseline quartile-stratified minimal clinically important difference estimates was superior to individual minimal clinically important difference estimates when compared with a gold standard comparator of important change. Pain. 2025 doi: 10.1097/j.pain.0000000000003492. [DOI] [PMC free article] [PubMed] [Google Scholar]
