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Differential Item Functioning Analyses of the Patient-Reported Outcomes Measurement Information System (PROMIS®) Measures: Methods, Challenges, Advances, and Future Directions

Published online by Cambridge University Press:  01 January 2025

Jeanne A. Teresi ,
Chun Wang ,
Marjorie Kleinman ,
Richard N. Jones  and
David J. Weiss
Jeanne A. Teresi*
Affiliation:
Columbia University Stroud Center Hebrew Home at Riverdale; RiverSpring Health Weill Cornell Medical Center New York State Psychiatric Institute
Chun Wang
Affiliation:
University of Washington College of Education
Marjorie Kleinman
Affiliation:
New York State Psychiatric Institute
Richard N. Jones
Affiliation:
Brown University
David J. Weiss
Affiliation:
University of Minnesota
*
Correspondence should be made to Jeanne A. Teresi, Columbia University Stroud Center, New York, NY, USA. Email: teresimeas@aol.com; jat61@cumc.columbia.edu
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Abstract

Several methods used to examine differential item functioning (DIF) in Patient-Reported Outcomes Measurement Information System (PROMIS®) measures are presented, including effect size estimation. A summary of factors that may affect DIF detection and challenges encountered in PROMIS DIF analyses, e.g., anchor item selection, is provided. An issue in PROMIS was the potential for inadequately modeled multidimensionality to result in false DIF detection. Section 1 is a presentation of the unidimensional models used by most PROMIS investigators for DIF detection, as well as their multidimensional expansions. Section 2 is an illustration that builds on previous unidimensional analyses of depression and anxiety short-forms to examine DIF detection using a multidimensional item response theory (MIRT) model. The Item Response Theory-Log-likelihood Ratio Test (IRT-LRT) method was used for a real data illustration with gender as the grouping variable. The IRT-LRT DIF detection method is a flexible approach to handle group differences in trait distributions, known as impact in the DIF literature, and was studied with both real data and in simulations to compare the performance of the IRT-LRT method within the unidimensional IRT (UIRT) and MIRT contexts. Additionally, different effect size measures were compared for the data presented in Section 2. A finding from the real data illustration was that using the IRT-LRT method within a MIRT context resulted in more flagged items as compared to using the IRT-LRT method within a UIRT context. The simulations provided some evidence that while unidimensional and multidimensional approaches were similar in terms of Type I error rates, power for DIF detection was greater for the multidimensional approach. Effect size measures presented in Section 1 and applied in Section 2 varied in terms of estimation methods, choice of density function, methods of equating, and anchor item selection. Despite these differences, there was considerable consistency in results, especially for the items showing the largest values. Future work is needed to examine DIF detection in the context of polytomous, multidimensional data. PROMIS standards included incorporation of effect size measures in determining salient DIF. Integrated methods for examining effect size measures in the context of IRT-based DIF detection procedures are still in early stages of development.

Keywords

PROMISmeasurementdifferential item functioningmultidimensional IRTeffect size estimates

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