Item banking: a generational change in patient-reported outcome measurement.

PURPOSE Patient-reported outcomes are traditionally measured with questionnaires and many have been developed to measure Vision-Related Activity Limitation (VRAL; visual disability or visual functioning), Symptoms, and Quality Of Life (QOL). These vary in quality and can be classified as First or Second Generation instruments. First generation instruments are characterized by simple summary scoring of ordinal responses, which precludes interval measurement. This problem is solved in second generation instruments where Rasch analysis is used to optimize psychometric properties. However, second generation instruments retain limitations; difficulties in comparing scores across instruments, limited applicability to populations and inability to adapt to change. A third generation approach to patient-reported outcomes measurement, item banking, can solve these problems. The aim of this project was to use Rasch analysis to calibrate all items from all instruments to form VRAL, Symptoms, and QOL Item Banks. METHODS Six hundred twenty-four people on the waiting list for cataract surgery were recruited. Each participant completed, by self-administration, a number of the 19 instruments. A total of 353 items were calibrated using Rasch analysis (Winsteps v3.67). The psychometric properties of each item bank were optimized; items fitting the Rasch model were retained (Infit and Outfit range, 0.50 to 1.50). RESULTS Items were sorted into the three traits; 226 tapped VRAL, 22 symptoms, and 60 QOL. Satisfactory measurement of each latent trait occurred with person separation of 8.11 for VRAL, 2.33 for Symptoms, and 3.20 for QOL. Rasch estimates of item difficulty were highly stable with an average standard error of 0.11 logits. CONCLUSIONS Item banks for the measurement of the latent traits of VRAL, symptoms, and QOL have been formed. New items can be added to enable evolution of measurement. Item banking facilitates accurate and precise measurement through computer adaptive testing. This approach provides common measurement scales, facilitating worldwide comparison of results.