@article{167f86af4a844ea7a34c4f3c66c3529a,
title = "An introduction to item response theory for patient-reported outcome measurement",
abstract = "The growing emphasis on patient-centered care has accelerated the demand for high-quality data from patient-reported outcome (PRO) measures. Traditionally, the development and validation of these measures has been guided by classical test theory. However, item response theory (IRT), an alternate measurement framework, offers promise for addressing practical measurement problems found in health-related research that have been difficult to solve through classical methods. This paper introduces foundational concepts in IRT, as well as commonly used models and their assumptions. Existing data on a combined sample (n = 636) of Korean American and Vietnamese American adults who responded to the High Blood Pressure Health Literacy Scale and the Patient Health Questionnaire-9 are used to exemplify typical applications of IRT. These examples illustrate how IRT can be used to improve the development, refinement, and evaluation of PRO measures. Greater use of methods based on this framework can increase the accuracy and efficiency with which PROs are measured.",
author = "Nguyen, {Tam H.} and Han, {Hae Ra} and Kim, {Miyong T.} and Chan, {Kitty S.}",
note = "Funding Information: Patient-reported outcomes (PROs) have long been a staple of clinical research [1, 2]. For many years, funding agencies and regulatory bodies such as the US Federal Drug Administration, Centers for Medicare & Medicaid Services, the British National Health Services, and more recently, the Patient Centered Outcomes Research Initiative, have pushed for a greater focus on outcomes that matter to patients as part of product testing, intervention trials, and evaluation of quality of care [3–5]. In recent years, the growing prominence of patient-centered care and value purchasing based on improving population health has accelerated the demand for high-quality data from PRO measures. PROs emphasize concepts such as quality of life, fatigue, and depression, which are best reported by patients themselves. Traditionally, the construction, scoring, refinement, and validation of PRO measures have been guided by classical test theory [6–8]. However, an alternative model-based theory called item response theory (IRT) offers promise for addressing practical measurement problems found in health-related research that have been difficult to solve through classical methods [9–11]. Used extensively in educational testing applications [12], this measurement framework has garnered great interest among health researchers. However, the key assumptions, properties, and potential applications of IRT for health-related research are not broadly known.",
year = "2014",
month = mar,
doi = "10.1007/s40271-013-0041-0",
language = "English (US)",
volume = "7",
pages = "23--35",
journal = "Patient",
issn = "1178-1653",
publisher = "Springer Science + Business Media",
number = "1",
}