Evidence map›Paper›PMID 37357308›Full record

ArticleHealth and quality of life outcomes2023

Applying multidimensional computerized adaptive testing to the MSQOL-54: a simulation study.

Andrea Giordano, Silvia Testa, Marta Bassi, Sabina Cilia, Antonio Bertolotto, Maria Esmeralda Quartuccio, Erika Pietrolongo, Monica Falautano, Monica Grobberio, Claudia Niccolai and 14 more

Abstract read
In one paragraph

Article in Health and quality of life outcomes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Andrea GiordanoUnit of Neuroepidemiology, Fondazione IRRCS Istituto Neurologico Carlo Besta, Via Celoria 11, Milan, 20133, Italy.ORCID http://orcid.org/0000-0003-4466-9788
Silvia TestaDepartment of Human and Social Sciences, University of Aosta Valley, Aosta, Italy.ORCID http://orcid.org/0000-0002-8732-4790
Marta BassiDepartment of Biomedical and Clinical Sciences, Università di Milano, Milan, Italy.ORCID http://orcid.org/0000-0003-0886-3989
Sabina CiliaDepartment of Territorial Activities, Azienda Sanitaria Provinciale, Health District, Catania, Italy.
Antonio BertolottoNeurology Unit & Regional Referral Multiple Sclerosis Centre (CReSM), University Hospital San Luigi Gonzaga, Orbassano, Italy.
Maria Esmeralda QuartuccioDepartment of Neuroscience, San Camillo-Forlanini Hospital, Rome, Italy.
Erika PietrolongoDepartment of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio, Chieti, Italy.ORCID http://orcid.org/0000-0002-6311-5994
Monica FalautanoPsychological Service - Neurological and Neurological Rehabilitation Units, IRCCS San Raffaele, Milan, Italy.
Monica GrobberioLaboratory of Clinical Neuropsychology, Psychology Unit, ASST Lariana, Como, Italy.
Claudia NiccolaiIRCCS Don Gnocchi Foundation, Florence, Italy.ORCID http://orcid.org/0000-0003-2746-7500
Beatrice AllegriMultiple Sclerosis Center, Neurology Unit, Hospital of Vaio, Fidenza, Italy.
Rosa Gemma ViterboAzienda Sanitaria Locale, ASL-BA, Bari, Italy.
Paolo ConfalonieriMultiple Sclerosis Center, Unit of Neuroimmunology and Neuromuscular Diseases, Fondazione IRRCS Istituto Neurologico Carlo Besta, Milan, Italy.
Ambra Mara GiovannettiUnit of Neuroepidemiology, Fondazione IRRCS Istituto Neurologico Carlo Besta, Via Celoria 11, Milan, 20133, Italy.ORCID http://orcid.org/0000-0002-7496-6727
Eleonora CoccoDepartment of Medical Science and Public Health, University of Cagliari, Cagliari, Italy.ORCID http://orcid.org/0000-0002-3878-8820
Maria Grazia GrassoMultiple Sclerosis Unit, IRCCS S. Lucia Foundation, Rome, Italy.
Alessandra LugaresiDipartimento di Scienze Biomediche e Neuromotorie, Università di Bologna, Bologna, Italy.ORCID http://orcid.org/0000-0003-2902-5589
Elisa FerrianiUOC Psicologia Ospedaliera, AUSL di Bologna, Bologna, Italy.
Ugo NocentiniDepartment of Clinical Sciences and Translational Medicine, University of Rome "Tor Vergata", Rome, Italy.ORCID http://orcid.org/0000-0001-7445-4441
Mauro ZaffaroniNeurologia ad indirizzo Neuroimmunologico - Centro Sclerosi Multipla, Ospedale di Gallarate - ASST della Valle Olona, Gallarate, Italy.
Alysha De LiveraMathematics and Statistics, La Trobe University, Melbourne, Australia.ORCID http://orcid.org/0000-0003-4981-4155
George JelinekNeuroepidemiology Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.ORCID http://orcid.org/0000-0001-6157-0910
Alessandra SolariUnit of Neuroepidemiology, Fondazione IRRCS Istituto Neurologico Carlo Besta, Via Celoria 11, Milan, 20133, Italy. alessandra.solari@istituto-besta.it.ORCID http://orcid.org/0000-0001-9930-7579
Rosalba RosatoDepartment of Psychology, University of Turin, Turin, Italy.ORCID http://orcid.org/0000-0002-4921-374X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Multiple Sclerosis Quality of Life-54 (MSQOL-54) is one of the most commonly-used MS-specific health-related quality of life (HRQOL) measures. It is a multidimensional, MS-specific HRQOL inventory, which includes the generic SF-36 core items, supplemented with 18 MS-targeted items. Availability of an adaptive short version providing immediate item scoring may improve instrument usability and validity. However, multidimensional computerized adaptive testing (MCAT) has not been previously applied to MSQOL-54 items. We thus aimed to apply MCAT to the MSQOL-54 and assess its performance.

methodsResponses from a large international sample of 3669 MS patients were assessed. We calibrated 52 (of the 54) items using bifactor graded response model (10 group factors and one general HRQOL factor). Then, eight simulations were run with different termination criteria: standard errors (SE) for the general factor and group factors set to different values, and change in factor estimates from one item to the next set at < 0.01 for both the general and the group factors. Performance of the MCAT was assessed by the number of administered items, root mean square difference (RMSD), and correlation.

resultsEight items were removed due to local dependency. The simulation with SE set to 0.32 (general factor), and no SE thresholds (group factors) provided satisfactory performance: the median number of administered items was 24, RMSD was 0.32, and correlation was 0.94.

conclusionsCompared to the full-length MSQOL-54, the simulated MCAT required fewer items without losing precision for the general HRQOL factor. Further work is needed to add/integrate/revise MSQOL-54 items in order to make the calibration and MCAT performance efficient also on group factors, so that the MCAT version may be used in clinical practice and research.

Indexed as

Computerized Adaptive TestingMultiple SclerosisQuality of LifeAdolescentAdultAgedAged, 80 and overComputer SimulationHumansMiddle AgedPsychometricsSurveys and QuestionnairesBifactor modelHealth-related quality of lifeItem response theoryMSQOL-54Multidimensional computerized adaptive testMultiple sclerosis

Identifiers

PMID37357308
PMCPMC10291804

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.