Linking pain items from two studies onto a common scale using item response theory.

This study examined two approaches to linking items from two pain surveys to form a single item bank with a common measurement scale. Secondary analysis of two independent surveys: Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials Survey with Main Survey (959 chronic pain patients; 42 pain items) and Pain Module (n=148; 36 pain items), and Center on Outcomes, Research and Education Survey (400 cancer patients; 43 pain items). There were common items included among the three data sets. In the first approach, all items were calibrated to an item response theory (IRT) model simultaneously, and in the second approach, items were calibrated separately and then the scales were transformed to a common metric. The two approaches produced similar linking results across the two sets of pain interference items because there was sufficient number of common items and large enough sample size. For pain intensity, simultaneous calibration yielded more stable results. Separated calibration yielded an unsatisfactory linking result for pain intensity because of a single common item with small sample size. The results suggested that a simultaneous IRT calibration method produces the more stable item parameters across independent samples, and hence, should be recommended for developing comprehensive item banks. Patient-reported health outcome surveys are often limited in sample sizes and the number of items owing to the difficulty of recruitment and the burden to the patients. As a result, the surveys either lack statistical power or are limited in scope. Using IRT methodology, survey data can be pooled to lend strength to each other to expand the scope and to increase the sample sizes.