ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2024
Estimating anchor-based minimal important change using longitudinal confirmatory factor analysis.
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 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Selecting the optimal MIC for five patient-reported outcome measures in patients with upper extremity injuries, using a systematic search and step-by-step decision tree.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Pooled it
- The latent difference score model is a viable alternative to arithmetic difference score-based anchor-driven minimal important change calculation.Journal of patient-reported outcomes · 2026Trial
- Responsiveness and minimal important change of the Dutch Adult Strabismus-20 Questionnaire (Dutch-AS-20).Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Estimating the minimal important change of single-item measures using the adjusted predictive modeling method or the longitudinal confirmatory factor analysis method.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- A weighted predictive modeling method for estimating thresholds of meaningful within-individual change for patient-reported outcomes.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Advances in the calculation of minimal important change estimates for patient-reported outcome measures.Nature reviews. Rheumatology · 2025Article
- Evaluating Barriers to Achieving the Minimal Important Change in Older Patients With Hip Fractures After Post-Acute Care.Geriatric orthopaedic surgery & rehabilitation · 2025Article
- Effect of present state bias on minimal important change estimates: a simulation study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Article
- Estimating anchor-based minimal important change using longitudinal confirmatory factor analysis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Article
- Interpretation of Change in Novel Digital Measures: A Statistical Review and Tutorial.Digital biomarkersReview
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe minimal important change (MIC) is defined as the smallest within-individual change in a patient-reported outcome measure (PROM) that patients on average perceive as important. We describe a method to estimate this value based on longitudinal confirmatory factor analysis (LCFA). The method is evaluated and compared with a recently published method based on longitudinal item response theory (LIRT) in simulated and real data. We also examined the effect of sample size on bias and precision of the estimate.
methodsWe simulated 108 samples with various characteristics in which the true MIC was simulated as the mean of individual MICs, and estimated MICs based on LCFA and LIRT. Additionally, both MICs were estimated in existing PROMIS Pain Behavior data from 909 patients. In another set of 3888 simulated samples with sample sizes of 125, 250, 500, and 1000, we estimated LCFA-based MICs.
resultsThe MIC was equally well recovered with the LCFA-method as using the LIRT-method, but the LCFA analyses were more than 50 times faster. In the Pain Behavior data (with higher scores indicating more pain behavior), an LCFA-based MIC for improvement was estimated to be 2.85 points (on a simple sum scale ranging 14-42), whereas the LIRT-based MIC was estimated to be 2.60. The sample size simulations showed that smaller sample sizes decreased the precision of the LCFA-based MIC and increased the risk of model non-convergence.
conclusionThe MIC can accurately be estimated using LCFA, but sample sizes need to be preferably greater than 125.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.