Item response theory assumptions were adequately met by the Oxford hip and knee scores

Objectives: To develop item response theory (IRT) models for the Oxford hip and knee scores which convert patient responses into continuous scores with quantifiable precision and provide these as web applications for efficient score conversion.Study Design and Setting: Data from the National Health Service patient-reported outcome measures program were used to test the assumptions of IRT (unidimensionality, monotonicity, local independence, and measurement invariance) before fitting models to preoper-ative response patterns obtained from patients undergoing primary elective hip or knee arthroplasty. The hip and knee datasets contained 321,147 and 355,249 patients, respectively.Results: Scree plots, Kaiser criterion analyses, and confirmatory factor analyses confirmed unidimensionality and Mokken analysis confirmed monotonicity of both scales. In each scale, all item pairs shared a residual correlation of < 0.20. At the test level, both scales showed measurement invariance by age and gender. Both scales provide precise measurement in preoperative settings but demonstrate poorer precision and ceiling effects in postoperative settings.Conclusion: We provide IRT parameters and web applications that can convert Oxford Hip Score or Oxford Knee Score response sets into continuous measurements and quantify individual measurement error. These can be used in sensitivity analyses or to administer trun-cated and individualized computerized adaptive tests. & COPY; 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This study aims to develop item response theory (IRT) models that convert patient responses into continuous scores and provide web applications for efficient score conversion. The study tested the assumptions of IRT with data from the National Health Service patient-reported outcome measures program and found that the hip and knee datasets demonstrated unidimensionality and measurement invariance. The models provided precise measurements in preoperative settings and can be used for sensitivity analyses.