Content-balancing strategy in bifactor computerized adaptive patient-reported outcome measurement

Most multidimensional patient-reported outcomes (PRO) measures are lengthy to complete. Computerized adaptive testing (CAT) that selects the most informative items can potentially reduce respondent burden without sacrificing measurement accuracy. The commonly used maximum Fisher information item selection method has been reported to lead to highly unbalanced item bank usage and potentially imprecise trait estimation. This study employs the content-balancing strategy in a bifactor-modeled CAT item selection and examines its impact on measurement accuracy and item bank usage.

Item responses from a population-based SF-36 survey were first calibrated using the bifactor graded response model. Four post hoc CATs using items and responses from the SF-36 data set were then created. The content-balancing strategy was adopted in the item selection procedure of the bifactor-modeled CAT. The measurement accuracy and usage of items of the CAT were compared between the tests with and without the content-balancing strategy.

The results indicate that the CAT implemented with the content-balancing strategy offers a better overall measurement accuracy of both the general health status and the two health domains (physical and mental) of the SF-36.

The content-balancing strategy helps the CAT–PRO to balance the selection of items and achieve improved measurement accuracy. Its implementation in real-time CAT administration to measure multidimensional PRO traits merits further studies.