Combining item response theory with multiple imputation to equate health assessment questionnaires. Issue 3 (9th December 2016)

• Record Type: Journal Article
• Authors: Gu, Chenyang; Gutman, Roee
• Is Part Of: Biometrics. Volume 73:Issue 3(2017)
• Page Start: 990
• Page End: 998
• Publication Date: 2016-12-09
• Subjects: Data augmentation -- Data fusion -- Hamiltonian Monte Carlo -- Item Response Theory -- Missing data -- Multiple imputation -- Predictive mean matching -- Statistical matching; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12638
• Languages: English
• ISSNs: 0006-341X

Abstract

Summary: The assessment of patients' functional status across the continuum of care requires a common patient assessment tool. However, assessment tools that are used in various health care settings differ and cannot be easily contrasted. For example, the Functional Independence Measure (FIM) is used to evaluate the functional status of patients who stay in inpatient rehabilitation facilities, the Minimum Data Set (MDS) is collected for all patients who stay in skilled nursing facilities, and the Outcome and Assessment Information Set (OASIS) is collected if they choose home health care provided by home health agencies. All three instruments or questionnaires include functional status items, but the specific items, rating scales, and instructions for scoring different activities vary between the different settings. We consider equating different health assessment questionnaires as a missing data problem, and propose a variant of predictive mean matching method that relies on Item Response Theory (IRT) models to impute unmeasured item responses. Using real data sets, we simulated missing measurements and compared our proposed approach to existing methods for missing data imputation. We show that, for all of the estimands considered, and in most of the experimental conditions that were examined, the proposed approach provides valid inferences, and generally has better coverages, relatively smaller biases, and shorter interval estimates. The proposed method is further illustrated using a real data set.