IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0169878.html
   My bibliography  Save this article

A Rasch Analysis of the Charcot-Marie-Tooth Neuropathy Score (CMTNS) in a Cohort of Charcot-Marie-Tooth Type 1A Patients

Author

Listed:
  • Wenjia Wang
  • Mickaël Guedj
  • Viviane Bertrand
  • Julie Foucquier
  • Elisabeth Jouve
  • Daniel Commenges
  • Cécile Proust-Lima
  • Niall P Murphy
  • Olivier Blin
  • Laurent Magy
  • Daniel Cohen
  • Shahram Attarian

Abstract

The Charcot-Marie-Tooth Neuropathy Score (CMTNS) was developed as a main efficacy endpoint for application in clinical trials of Charcot-Marie-Tooth disease type 1A (CMT1A). However, the sensitivity of the CMTNS for measuring disease severity and progression in CMT1A patients has been questioned. Here, we applied a Rasch analysis in a French cohort of patients to evaluate the psychometrical properties of the CMTNS. Overall, our analysis supports the validity of the CMTNS for application to CMT1A patients though with some limitations such as certain items of the CMTNS being more suitable for moderate to severe forms of the disease, and some items being disordered. We suggest that additional items and/or categories be considered to better assess mild-to-moderate patients.

Suggested Citation

  • Wenjia Wang & Mickaël Guedj & Viviane Bertrand & Julie Foucquier & Elisabeth Jouve & Daniel Commenges & Cécile Proust-Lima & Niall P Murphy & Olivier Blin & Laurent Magy & Daniel Cohen & Shahram Attar, 2017. "A Rasch Analysis of the Charcot-Marie-Tooth Neuropathy Score (CMTNS) in a Cohort of Charcot-Marie-Tooth Type 1A Patients," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0169878
    DOI: 10.1371/journal.pone.0169878
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169878
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0169878&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0169878?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David Andrich, 2010. "Sufficiency and Conditional Estimation of Person Parameters in the Polytomous Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 292-308, June.
    2. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    3. Mair, Patrick & Hatzinger, Reinhold, 2007. "Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i09).
    4. Lee Cronbach, 1951. "Coefficient alpha and the internal structure of tests," Psychometrika, Springer;The Psychometric Society, vol. 16(3), pages 297-334, September.
    5. Karl Christensen & Jakob Bjorner & Svend Kreiner & Jørgen Petersen, 2002. "Testing unidimensionality in polytomous Rasch models," Psychometrika, Springer;The Psychometric Society, vol. 67(4), pages 563-574, December.
    6. Erling Andersen, 1973. "A goodness of fit test for the rasch model," Psychometrika, Springer;The Psychometric Society, vol. 38(1), pages 123-140, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniela Raccanello & Giada Vicentini & Elena Trifiletti & Roberto Burro, 2020. "A Rasch Analysis of the School-Related Well-Being (SRW) Scale: Measuring Well-Being in the Transition from Primary to Secondary School," IJERPH, MDPI, vol. 18(1), pages 1-17, December.
    2. Gerhard Tutz & Gunther Schauberger, 2015. "A Penalty Approach to Differential Item Functioning in Rasch Models," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 21-43, March.
    3. Clemens Draxler & Andreas Kurz & Can Gürer & Jan Philipp Nolte, 2024. "An Improved Inferential Procedure to Evaluate Item Discriminations in a Conditional Maximum Likelihood Framework," Journal of Educational and Behavioral Statistics, , vol. 49(3), pages 403-430, June.
    4. César Merino-Soto & Gina Chávez-Ventura & Verónica López-Fernández & Guillermo M. Chans & Filiberto Toledano-Toledano, 2022. "Learning Self-Regulation Questionnaire (SRQ-L): Psychometric and Measurement Invariance Evidence in Peruvian Undergraduate Students," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    5. Anja C. Rohenkohl & Monika Bullinger & Andreas M. Pleil & Levente Kriston & Julia H. Quitmann, 2016. "A Brief Version of the Quality of Life in Short Stature Youth Questionnaire - the QoLISSY-Brief," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 9(4), pages 971-984, December.
    6. P. A. Ferrari & S. Salini, 2008. "Measuring Service Quality: The Opinion of Europeans about Utilities," Working Papers 2008.36, Fondazione Eni Enrico Mattei.
    7. Salzberger, Thomas & Newton, Fiona J. & Ewing, Michael T., 2014. "Detecting gender item bias and differential manifest response behavior: A Rasch-based solution," Journal of Business Research, Elsevier, vol. 67(4), pages 598-607.
    8. David J. Hessen, 2023. "Fitting and Testing Log-Linear Subpopulation Models with Known Support," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 917-939, September.
    9. C. Glas & Anna Dagohoy, 2007. "A Person Fit Test For Irt Models For Polytomous Items," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 159-180, June.
    10. Piero Veronese & Eugenio Melilli, 2021. "Confidence Distribution for the Ability Parameter of the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 131-166, March.
    11. Timo Bechger & Gunter Maris, 2015. "A Statistical Test for Differential Item Pair Functioning," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 317-340, June.
    12. repec:jss:jstsof:36:c01 is not listed on IDEAS
    13. Georg Gittler & Gerhard Fischer, 2011. "IRT-Based Measurement of Short-Term Changes of Ability, With an Application to Assessing the “Mozart Effectâ€," Journal of Educational and Behavioral Statistics, , vol. 36(1), pages 33-75, February.
    14. Robert Zwitser & Gunter Maris, 2015. "Conditional Statistical Inference with Multistage Testing Designs," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 65-84, March.
    15. Gerhard Tutz, 2021. "Hierarchical Models for the Analysis of Likert Scales in Regression and Item Response Analysis," International Statistical Review, International Statistical Institute, vol. 89(1), pages 18-35, April.
    16. repec:jss:jstsof:35:i12 is not listed on IDEAS
    17. Bartolucci, Francesco & Bacci, Silvia & Gnaldi, Michela, 2014. "MultiLCIRT: An R package for multidimensional latent class item response models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 971-985.
    18. Bacci, Silvia & Fabbricatore, Rosa & Iannario, Maria, 2023. "Multilevel IRT models for the analysis of satisfaction for distance learning during the Covid-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    19. Clemens Draxler, 2010. "Sample Size Determination for Rasch Model Tests," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 708-724, December.
    20. Carla Sabariego & Cornelia Oberhauser & Aleksandra Posarac & Jerome Bickenbach & Nenad Kostanjsek & Somnath Chatterji & Alana Officer & Michaela Coenen & Lay Chhan & Alarcos Cieza, 2015. "Measuring Disability: Comparing the Impact of Two Data Collection Approaches on Disability Rates," IJERPH, MDPI, vol. 12(9), pages 1-23, August.
    21. Maud Dampérat & Ping Lei & Florence Jeannot, 2019. "IRT Approach for rating scales: applications for normal and non-normal distributions," Post-Print hal-04325043, HAL.
    22. Rikkert M. van der Lans & Ridwan Maulana & Michelle Helms-Lorenz & Carmen-María Fernández-García & Seyeoung Chun & Thelma de Jager & Yulia Irnidayanti & Mercedes Inda-Caro & Okhwa Lee & Thys Coetze, 2021. "Student Perceptions of Teaching Quality in Five Countries: A Partial Credit Model Approach to Assess Measurement Invariance," SAGE Open, , vol. 11(3), pages 21582440211, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0169878. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.