The PROMIS Physical Function item bank was calibrated to a standardized metric and shown to improve measurement efficiency

Abstract

Objective: To document the development and psychometric evaluation of the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function (PF) item bank and static instruments.

Study design and setting: The items were evaluated using qualitative and quantitative methods. A total of 16,065 adults answered item subsets (n>2,200/item) on the Internet, with oversampling of the chronically ill. Classical test and item response theory methods were used to evaluate 149 PROMIS PF items plus 10 Short Form-36 and 20 Health Assessment Questionnaire-Disability Index items. A graded response model was used to estimate item parameters, which were normed to a mean of 50 (standard deviation [SD]=10) in a US general population sample.

Results: The final bank consists of 124 PROMIS items covering upper, central, and lower extremity functions and instrumental activities of daily living. In simulations, a 10-item computerized adaptive test (CAT) eliminated floor and decreased ceiling effects, achieving higher measurement precision than any comparable length static tool across four SDs of the measurement range. Improved psychometric properties were transferred to the CAT's superior ability to identify differences between age and disease groups.

Conclusion: The item bank provides a common metric and can improve the measurement of PF by facilitating the standardization of patient-reported outcome measures and implementation of CATs for more efficient PF assessments over a larger range.

Keywords: Computerized adaptive test; Health status; Item response theory; Patient-reported outcomes; Physical function; Questionnaire.

Figure 1. Measurement precision and range of two established static tools, PROMIS static forms, a simulated PROMIS PF-CAT with 10 items, and the entire item bank

The Y-axis shows the standard error of measurement, the X-axis the Θ-value. The graph shows the precision of the test which can be expected at a particular level of Physical Function (latent trait, Θ) based on test information (static forms) or simulation studies (CAT)

Figure 2

Proposed CAT Algorithm