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Published on: January 7, 2019
Content-balancing strategy in bifactor computerized adaptive patient-reported outcome measurement
Yi Zheng1, Chih-Hung Chang, Hua-Hua Chang
1University of Illinois at Urbana-Champaign, 1310 S. Sixth St Rm 210, Champaign, IL 61820, USA. yizheng1@illinois.edu
Summary
A new content-balancing strategy improves computerized adaptive testing (CAT) for patient-reported outcomes (PRO). This method enhances measurement accuracy and balances item usage in health status assessments.
Area of Science:
- Health Outcomes Research
- Psychometrics
- Health Services Research
Background:
- Multidimensional patient-reported outcomes (PRO) measures are often lengthy.
- Computerized adaptive testing (CAT) can reduce respondent burden by selecting informative items.
- Traditional item selection methods may lead to unbalanced item bank usage and imprecise trait estimation.
Purpose of the Study:
- To evaluate the impact of a content-balancing strategy in bifactor-modeled CAT on measurement accuracy and item bank usage.
- To compare the performance of CAT with and without the content-balancing strategy.
- To assess the effectiveness of content-balancing for multidimensional PRO measurement.
Main Methods:
- Item responses from the SF-36 survey were calibrated using the bifactor graded response model.
- Four post hoc CATs were created using SF-36 data.
- The content-balancing strategy was implemented in the bifactor-modeled CAT item selection for comparison.
Main Results:
- CAT with the content-balancing strategy demonstrated superior overall measurement accuracy.
- Improved accuracy was observed for general health status and its physical and mental health domains.
- The content-balancing strategy led to more balanced item bank usage.
Conclusions:
- The content-balancing strategy effectively balances item selection in CAT-PRO.
- This strategy enhances measurement accuracy for multidimensional PRO traits.
- Further research into real-time CAT administration using this strategy is warranted.
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