title: Content-balancing strategy in bifactor computerized adaptive patient-reported outcome measurement.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: University of Illinois at Urbana-Champaign, 1310 S. Sixth St Rm 210, Champaign, IL 61820, USA. yizheng1@illinois.edu
authors.forename: Yi
authors.initials: Y
authors.lastname: Zheng
authors.forename: Chih-Hung
authors.initials: CH
authors.lastname: Chang
authors.forename: Hua-Hua
authors.initials: HH
authors.lastname: Chang
mesh_terms: D000203:Activities of Daily Living; D003627:Data Interpretation, Statistical; D005260:Female; D006304:Health Status; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D017060:Patient Satisfaction; D011788:Quality of Life; D015203:Reproducibility of Results; D012984:Software; D011795:Surveys and Questionnaires