Enhancing Measurement Precision of Patient-Reported Outcomes Using Item Response Theory

Abstract

Accurate assessment of patient-reported outcomes (PROs) is essential for informing clinical decision-making and guiding health policy. Item Response Theory (IRT) enhances measurement by providing detailed evidence on item discrimination, difficulty, fairness, and precision, consistent with COSMIN guidelines. A quantitative, cross-sectional design was employed, involving 500 adult patients attending outpatient facilities across public, private, and community-based healthcare centers in southern Ghana. Stratified random sampling was used to ensure representativeness across settings. Psychometric evaluation combined CTT analyzes Cronbach's alpha, item-total correlations, and factor analysis with IRT modeling, specifically the graded response model (GRM). CTT analyses indicated good internal consistency (Cronbach's α = .84), while IRT modeling (graded response model) showed higher reliability (marginal reliability = 0.91) and revealed patterns of precision across the health spectrum. IRT-based scores were meaningfully associated with treatment adherence (β = .45), quality of life (β = .41), and self-reported health status (β = .38), illustrating predictive validity. Differential item functioning analyses indicated limited subgroup bias. Integrating CTT and IRT strengthens the rigor, precision, and fairness of PRO measurement. IRT-calibrated instruments demonstrate practical value for clinical monitoring and health system evaluation and are recommended for routine implementation in diverse healthcare settings.