Accounting for Multidimensionality in Item Responses in Patient-Centered and Patient Reported Outcomes Measurement

Abstract

Background: More robust and rigorous psychometric models, such as Item Response Theory (IRT) models, have been advocated for applications measuring health sciences outcomes. However, there are challenges to the use of IRT models with health assessments. In particular, item responses from measuring health-related outcomes are typically determined by multiple traits or dimensions. This multidimensionality can be caused by various factors including designed multidimensional structure to the instrument, heterogeneity in item content, and from other sources such as differential item functioning in subpopulations and individual differences in response styles to survey items and rating scales. Objectives: This paper discusses different extensions to IRT models that can be used to account for different types of multidimensionality as well as the use of Bayesian methods with person-centered medicine research.Methods: Use of the SAS PROC MCMC platform for implementing Bayesian analyses is illustrated to estimate and analyze IRT applications to health-related assessments. Results: PROC MCMC involves a straightforward translation of the response probability model along with specifications of the model parameters and prior distributions for the model parameters. Conclusions: Bayesian analysis of multidimensional IRT models is more accessible to researchers and scale developers in measuring health sciences outcomes for person-centered medicine research.