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Solving the Tower of Babel Problem for Patient-Reported Outcome Measures

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  • Jakob Bue Bjorner

    (QualityMetric Incorporated, LLC
    University of Copenhagen
    National Research Centre for the Working Environment)

Abstract

The PROsetta Stone Project, summarized in this issue by Schalet et al. (Psychometrika 86, 2021), is a major step forward in enabling comparability between different patient-reported outcomes measures. Schalet et al. clearly describe the psychometric methods used in the PROsetta Stone project and other projects from the Patient-Reported Outcomes Measurement Information System (PROMIS): linking based on unidimensional item response theory (IRT), equipercentile linking, and calibrated projection based on multidimensional IRT. Analyses in a validation data set and simulation studies provide strong support that the linking methods are robust when basic assumptions are fulfilled. The links already established will be of great value to the field, and the methodology described by Schalet et al. will hopefully inspire the next series of linking studies. Among potential improvements that should be considered by new studies are: (1) a thorough evaluation of the content of the measures to be linked to better guide the evaluation of measurement assumptions, (2) improvements in the design of linking studies such as selection of the optimal sample to provide data in the score ranges where linking precision is most critical and using counterbalanced designs to control for order effects. Finally, it may be useful to consider how the linking algorithms are used in subsequent data analyses. Analytic strategies based on plausible values or latent regression IRT models may be preferable to the simple transformation of scores from one patient at the time.

Suggested Citation

  • Jakob Bue Bjorner, 2021. "Solving the Tower of Babel Problem for Patient-Reported Outcome Measures," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 747-753, September.
    • Handle: RePEc:spr:psycho:v:86:y:2021:i:3:d:10.1007_s11336-021-09778-x
    DOI: 10.1007/s11336-021-09778-x
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    1. Robert Mislevy, 1991. "Randomization-based inference about latent variables from complex samples," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 177-196, June.
    2. Benjamin D. Schalet & Sangdon Lim & David Cella & Seung W. Choi, 2021. "Linking Scores with Patient-Reported Health Outcome Instruments:A VALIDATION STUDY AND COMPARISON OF THREE LINKING METHODS," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 717-746, September.
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