IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0092367.html
   My bibliography  Save this article

Calibration of the PROMIS Physical Function Item Bank in Dutch Patients with Rheumatoid Arthritis

Author

Listed:
  • Martijn A H Oude Voshaar
  • Peter M ten Klooster
  • Cees A W Glas
  • Harald E Vonkeman
  • Erik Taal
  • Eswar Krishnan
  • Hein J Bernelot Moens
  • Maarten Boers
  • Caroline B Terwee
  • Piet L C M van Riel
  • Mart A F J van de Laar

Abstract

Objective: To calibrate the Dutch-Flemish version of the PROMIS physical function (PF) item bank in patients with rheumatoid arthritis (RA) and to evaluate cross-cultural measurement equivalence with US general population and RA data. Methods: Data were collected from RA patients enrolled in the Dutch DREAM registry. An incomplete longitudinal anchored design was used where patients completed all 121 items of the item bank over the course of three waves of data collection. Item responses were fit to a generalized partial credit model adapted for longitudinal data and the item parameters were examined for differential item functioning (DIF) across country, age, and sex. Results: In total, 690 patients participated in the study at time point 1 (T2, N = 489; T3, N = 311). The item bank could be successfully fitted to a generalized partial credit model, with the number of misfitting items falling within acceptable limits. Seven items demonstrated DIF for sex, while 5 items showed DIF for age in the Dutch RA sample. Twenty-five (20%) items were flagged for cross-cultural DIF compared to the US general population. However, the impact of observed DIF on total physical function estimates was negligible. Discussion: The results of this study showed that the PROMIS PF item bank adequately fit a unidimensional IRT model which provides support for applications that require invariant estimates of physical function, such as computer adaptive testing and targeted short forms. More studies are needed to further investigate the cross-cultural applicability of the US-based PROMIS calibration and standardized metric.

Suggested Citation

  • Martijn A H Oude Voshaar & Peter M ten Klooster & Cees A W Glas & Harald E Vonkeman & Erik Taal & Eswar Krishnan & Hein J Bernelot Moens & Maarten Boers & Caroline B Terwee & Piet L C M van Riel & Mar, 2014. "Calibration of the PROMIS Physical Function Item Bank in Dutch Patients with Rheumatoid Arthritis," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
  • Handle: RePEc:plo:pone00:0092367
    DOI: 10.1371/journal.pone.0092367
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092367
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0092367&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0092367?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. Cees Glas, 1999. "Modification indices for the 2-PL and the nominal response model," Psychometrika, Springer;The Psychometric Society, vol. 64(3), pages 273-294, September.
    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. A. Béguin & C. Glas, 2001. "MCMC estimation and some model-fit analysis of multidimensional IRT models," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 541-561, December.
    2. 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.
    3. Albert Maydeu-Olivares & Harry Joe, 2006. "Limited Information Goodness-of-fit Testing in Multidimensional Contingency Tables," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 713-732, December.
    4. Scott Monroe, 2019. "Estimation of Expected Fisher Information for IRT Models," Journal of Educational and Behavioral Statistics, , vol. 44(4), pages 431-447, August.
    5. Daniel Oberski & Geert Kollenburg & Jeroen Vermunt, 2013. "A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 267-279, September.
    6. Ting Wang & Carolin Strobl & Achim Zeileis & Edgar C. Merkle, 2018. "Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 132-155, March.
    7. Wim J. van der Linden, 2009. "A Bivariate Lognormal Response-Time Model for the Detection of Collusion Between Test Takers," Journal of Educational and Behavioral Statistics, , vol. 34(3), pages 378-394, September.
    8. Felix Zimmer & Clemens Draxler & Rudolf Debelak, 2023. "Power Analysis for the Wald, LR, Score, and Gradient Tests in a Marginal Maximum Likelihood Framework: Applications in IRT," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1249-1298, December.
    9. Ivo Ponocny, 2001. "Nonparametric goodness-of-fit tests for the rasch model," Psychometrika, Springer;The Psychometric Society, vol. 66(3), pages 437-459, September.
    10. Mark Reiser & Silvia Cagnone & Junfei Zhu, 2023. "An Extended GFfit Statistic Defined on Orthogonal Components of Pearson’s Chi-Square," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 208-240, March.
    11. Guastadisegni, Lucia & Cagnone, Silvia & Moustaki, Irini & Vasdekis, Vassilis, 2022. "Use of the Lagrange multiplier test for assessing measurement invariance under model misspecification," LSE Research Online Documents on Economics 110358, London School of Economics and Political Science, LSE Library.
    12. Ting Wang & Benjamin Graves & Yves Rosseel & Edgar C. Merkle, 2022. "Computation and application of generalized linear mixed model derivatives using lme4," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1173-1193, September.
    13. Yang Liu & Ji Seung Yang & Alberto Maydeu-Olivares, 2019. "Restricted Recalibration of Item Response Theory Models," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 529-553, June.

    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:pone00:0092367. 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.