Objective: The aim was to develop two disease- and treatment-related knowledge about RA (DataK-RA) short forms using item response theory-based linear optimal test design.
Methods: We used the open source Excel add-in solver to program a linear optimization algorithm to develop two short forms from the DataK-RA item bank. The algorithm was instructed to optimize precision (i.e. reliability) of the scores for both short forms, subject to a number of constraints that served to ensure that each short form would include unique items and that the short forms would have similar psychometric properties. Agreement among item response theory scores obtained from the different short forms was assessed using the Bland-Altman method and Student's paired t-test. Construct validity and relative efficiency of the short forms was evaluated by relating the score to age, sex and educational attainment.
Results: Two short forms were derived from the DataK-RA item bank that satisfied all content constraints. Both short forms included 15 unique items and yielded reliable scores (r > 0.70), with low ceiling and floor effects. The short forms yielded statistically indistinguishable mean scores according to Student's paired t-test and Bland-Altman analysis. Scores on short forms 1 and 2 were associated with age, sex and educational attainment to a similar extent.
Conclusion: In this study, we developed two DataK-RA short forms with unique items, yet similar psychometric properties, that can be used to assess patients pre- and post-test interventions aimed at improving disease-related knowledge in RA patients.
Keywords: item response theory; patient education; patient knowledge; rheumatoid arthritis; short form.
© The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Rheumatology.