ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2026
Estimating the minimal important change of single-item measures using the adjusted predictive modeling method or the longitudinal confirmatory factor analysis method.
Article in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRecently developed minimal important change (MIC) estimation methods recover the mean individual MIC in a sample. These methods are the adjusted predictive modeling (APM) method and the longitudinal confirmatory factor analysis (LCFA) method. Both methods require LCFA of patient-reported outcome measure (PROM) data. In the APM-method, LCFA is used to estimate the reliability of the transition ratings, whereas in the LCFA-method, LCFA is used to estimate the latent MIC. However, LCFA cannot be performed if the PROM is a single item measure (SIM). Adding an auxiliary variable, that is correlated with the PROM, to the LCFA-model may be a solution. We developed three different LCFA-models in which an auxiliary variable is included. In this simulation study, we assessed the performance of the APM- and LCFA-methods to recover the true MIC of an SIM. We applied both methods to a real dataset in which the SIM was a numeric rating scale for pain.
methodsWe simulated 15,552 samples, varying in 11 parameters, and estimated the APM-based and LCFA-based MICs.
resultsThe APM-method performed well, except if the proportion improved was high or low, and the present state bias (PSB) was high. The LCFA-method performed well, irrespective of the proportion improved and the PSB. In the real data, the LCFA-based MIC was 17 (on a 100-point scale), whereas the estimated APM-based MIC was 4 points higher, probably due to a high proportion improved and a high PSB.
conclusionThe MIC of an SIM can be accurately estimated using an auxiliary PROM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.