Using subdomain-specific item sets affected PROMIS physical function scores differently in cardiology and rheumatology patients

Abstract

Objectives: The Patient-Reported Outcomes Measurement Information System (PROMIS) physical function (PF) item bank has been developed to standardize patient-reported PF across medical fields. However, evidence of scoring equivalence across cardiology and rheumatology patients is still missing. Therefore, this study aims to investigate both (1) the extent of disease-related differential item functioning (DIF) and (2) the impact of the disease group on using subdomain-specific item sets for generating PROMIS PF scores in cardiology and rheumatology patients. Study Design and Setting: Ordinal regression was used to evaluate DIF between cardiology (n 5 201) and rheumatology (n 5 200) inpatients. To explore the disease-specific impact of PF subdomains on scoring, we compared scores derived from the full item bank with scores derived from subdomain-specific item sets for each disease group. Results: DIF was detected in 18 items, predominately from the upper extremity subdomain. When upper extremity items were used, cardiology patients reached systematically higher scores than using the full item bank. Rheumatology patients scored substantially higher when mobility items were used. Conclusion: Applying the PROMIS PF metric to disease-specific item sets including items from differing subdomains may lead to biased comparisons of PF levels across disease groups. Disease-specific item parameters should be provided for items showing DIF, and subdomain-related content balancing is recommended for scoring the generic PROMIS PF construct.

Suitability of the animated activity questionnaire for use as computer adaptive test: establishing the AAQ-CAT

“…Important to consider in relation to CAT administration is that the individual items of the AAQ cover somewhat different aspects of physical activity limitations, i.e., climbing stairs (items 1 and 2), walking (items 3 to 7), rising and sitting down (items 9 to 15), but also activities that require fine motor skills next to hip joint mobility, such as picking up an object from the floor as well as putting on and taking off shoes (items 8, 16, and 17, respectively). Thus, when applied as CAT, content validity of the AAQ might be reduced in case one or more of these aspects are skipped due to the automatized CAT algorithm [ 39 ]. Content validity means that a measure represents all aspects of the construct of interest.…”

“…In the 10-item CAT, only one item (item 8) was never used; in the 17-item CAT, all items were used (lowest exposure rate was 17% for item 15). Content balancing has been suggested to be a potential solution when reduced content validity causes systematic bias in CAT assessments, i.e., when the items of a scale appear to measure distinct sub-constructs [ 23 , 39 ]. Nevertheless, our analyses did not indicate any systematic bias when administering the AAQ as CAT.…”

“…With regard to DIF by country, we compared Brazilin data versus all other countries, because measurement invariance across the other 6 countries was demonstrated before [ 17 ]. To identify DIF, ordinal logistic regression was applied [ 39 ].…”

Physical performance tasks were linked to the PROMIS physical function metric in patients undergoing hemodialysis

“…For the case of a low percentage of uncontaminated correlations (PUC < 0.8), an explained common variance > 0.6 and an omegaH value > 0.7 have been suggested as reasonable thresholds [ 36 ]; loadings on the general factor ≥ 0.30 were defined as salient [ 29 ]. Measurement invariance with respect to age, sex, medical condition, dialysis duration, and region was investigated using ordinal logistic regression for examining differential item functioning (DIF) [ 37 ].…”

Item response theory.

“…Moreover, because of the IRT item and person invariance properties, IRT models can be used to either place items from different instruments onto a common scale, or place individuals who responded to different items onto a common scale (Boulton et al, 2019;Lee et al, 2020;Schalet et al, 2020;Victorson et al, 2019). In turn, this facilitates the analysis of differential item functioning across demographic groups (i.e., exploring whether the items measure the same latent variable in the same way across different groups (Crins et al, 2019;Flens et al, 2021;Hays et al, 2018;Liegl et al, 2020;Taple et al, 2020)), as well as the creation of "item banks" that can be administered efficiently via computerized adaptive testing (Gibbons et al, 2020;Granziol et al, 2022;Moore et al, 2019;Thomas et al, 2020).…”