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Linking Scores with Patient-Reported Health Outcome Instruments:A VALIDATION STUDY AND COMPARISON OF THREE LINKING METHODS

Author

Listed:
  • Benjamin D. Schalet

    (Northwestern University, Feinberg School of Medicine)

  • Sangdon Lim

    (The University of Texas at Austin)

  • David Cella

    (Northwestern University, Feinberg School of Medicine)

  • Seung W. Choi

    (The University of Texas at Austin)

Abstract

The psychometric process used to establish a relationship between the scores of two (or more) instruments is generically referred to as linking. When two instruments with the same content and statistical test specifications are linked, these instruments are said to be equated. Linking and equating procedures have long been used for practical benefit in educational testing. In recent years, health outcome researchers have increasingly applied linking techniques to patient-reported outcome (PRO) data. However, these applications have some noteworthy purposes and associated methodological questions. Purposes for linking health outcomes include the harmonization of data across studies or settings (enabling increased power in hypothesis testing), the aggregation of summed score data by means of score crosswalk tables, and score conversion in clinical settings where new instruments are introduced, but an interpretable connection to historical data is needed. When two PRO instruments are linked, assumptions for equating are typically not met and the extent to which those assumptions are violated becomes a decision point around how (and whether) to proceed with linking. We demonstrate multiple linking procedures—equipercentile, unidimensional IRT calibration, and calibrated projection—with the Patient-Reported Outcomes Measurement Information System Depression bank and the Patient Health Questionnaire-9. We validate this link across two samples and simulate different instrument correlation levels to provide guidance around which linking method is preferred. Finally, we discuss some remaining issues and directions for psychometric research in linking PRO instruments.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:psycho:v:86:y:2021:i:3:d:10.1007_s11336-021-09776-z
    DOI: 10.1007/s11336-021-09776-z
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    References listed on IDEAS

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    1. Li Cai, 2015. "Lord–Wingersky Algorithm Version 2.0 for Hierarchical Item Factor Models with Applications in Test Scoring, Scale Alignment, and Model Fit Testing," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 535-559, June.
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    5. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    6. Albano, Anthony D., 2016. "equate: An R Package for Observed-Score Linking and Equating," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i08).
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    Cited by:

    1. Bryce B. Reeve & Ron D. Hays, 2021. "Guest Editors’ Introduction to the Invited Special Section," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 671-673, September.
    2. 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.

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