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Background  Modern psychometric methods based on item response theory (IRT) can be used to develop adaptive measures of health-related quality of life (HRQL). Adaptive assessment requires an item bank for each domain of HRQL. The purpose of this study was to develop item banks for five domains of HRQL relevant to arthritis.    Methods  About 1,400 items were drawn from published questionnaires or developed from focus groups and individual interviews and classified into 19 domains of HRQL. We selected the following 5 domains relevant to arthritis and related conditions: Daily Activities, Walking, Handling Objects, Pain or Discomfort, and Feelings. Based on conceptual criteria and pilot testing, 219 items were selected for further testing. A questionnaire was mailed to patients from two hospital-based clinics and a stratified random community sample. Dimensionality of the domains was assessed through factor analysis. Items were analyzed with the Generalized Partial Credit Model as implemented in Parscale. We used graphical methods and a chi-square test to assess item fit. Differential item functioning was investigated using logistic regression.    Results  Data were obtained from 888 individuals with arthritis. The five domains were sufficiently unidimensional for an IRT-based analysis. Thirty-one items were deleted due to lack of fit or differential item functioning. Daily Activities had the narrowest range for the item location parameter (-2.24 to 0.55) and Handling Objects had the widest range (-1.70 to 2.27). The mean (median) slope parameter for the items ranged from 1.15 (1.07) in Feelings to 1.73 (1.75) in Walking. The final item banks are comprised of 31–45 items each.    Conclusion  We have developed IRT-based item banks to measure HRQL in 5 domains relevant to arthritis. The items in the final item banks provide adequate psychometric information for a wide range of functional levels in each domain.

Assessment of health-related quality of life in arthritis: conceptualization and development of five item banks using item response theory