An Introduction to Item Response Theory for Patient-Reported Outcome Measurement

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 mea...

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Vydáno v:The patient : patient-centered outcomes research Ročník 7; číslo 1; s. 23 - 35
Hlavní autoři: Nguyen, Tam H., Han, Hae-Ra, Kim, Miyong T., Chan, Kitty S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.03.2014
Springer Nature B.V
Témata:
Asian - psychology
Diabetes
Health Administration
Health Economics
Health Literacy
Health Services Research - methods
Health Status
Humans
Hypertension - ethnology
Mathematical models
Medicine
Medicine & Public Health
Mental Health
Patient Outcome Assessment
Patient-Centered Care
Pharmacoeconomics and Health Outcomes
Practical Application
Product testing
Public Health
Quality of life
Quality of Life Research
Software
Theory
Differential Item Functioning
Item Bank
Item Response Theory
Item Parameter
Item Response Theory Model
ISSN:1178-1653, 1178-1661
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Shrnutí: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.
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ISSN:1178-1653
1178-1661
DOI:10.1007/s40271-013-0041-0