Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement

Edelen, Maria Orlando; Reeve, Bryce B.

doi:10.1007/s11136-007-9198-0

Cited by 630 publications

(591 citation statements)

References 51 publications

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“…IRT is a family of mathematical models that allow researchers to assign unique values (i.e., parameters) to each item based on how likely people with different levels of the measured construct are to endorse reach response category. 21,22 In the current study, the results of each linking method showed a high degree of similarity, consistent with previous reports for the domains of depression 23 , anxiety 24 , and fatigue 25 . Here we report only the results of the fixed IRT calibration, consistent with other published reports.…”

Section: Resultssupporting

confidence: 91%

Establishing a Common Metric for Physical Function: Linking the HAQ-DI and SF-36 PF Subscale to PROMIS® Physical Function

Schalet

Revicki²,

Cook

et al. 2015

J GEN INTERN MED

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Section: Resultssupporting

confidence: 91%

Establishing a Common Metric for Physical Function: Linking the HAQ-DI and SF-36 PF Subscale to PROMIS® Physical Function

Schalet

Revicki²,

Cook

et al. 2015

J GEN INTERN MED

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“…The relationship between patients' response to an item and their level of disability, here called IRT disability, was modeled as ordered categorical data, and item characteristic curves (ICC) are used to quantify and visualize it (17). Observed scores for each EDSS item were modeled describing the probability of a given score as a function of patients' disability variable using a logistic model:…”

Section: Modeling Methodsologymentioning

confidence: 99%

Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis

Novakovic

Krekels

Munafo

et al. 2016

AAPS J

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ABSTRACT.In this study, we report the development of the first item response theory (IRT) model within a pharmacometrics framework to characterize the disease progression in multiple sclerosis (MS), as measured by Expanded Disability Status Score (EDSS). Data were collected quarterly from a 96-week phase III clinical study by a blinder rater, involving 104,206 item-level observations from 1319 patients with relapsing-remitting MS (RRMS), treated with placebo or cladribine. Observed scores for each EDSS item were modeled describing the probability of a given score as a function of patients' (unobserved) disability using a logistic model. Longitudinal data from placebo arms were used to describe the disease progression over time, and the model was then extended to cladribine arms to characterize the drug effect. Sensitivity with respect to patient disability was calculated as Fisher information for each EDSS item, which were ranked according to the amount of information they contained. The IRT model was able to describe baseline and longitudinal EDSS data on item and total level. The final model suggested that cladribine treatment significantly slows disease-progression rate, with a 20% decrease in disease-progression rate compared to placebo, irrespective of exposure, and effects an additional exposure-dependent reduction in disability progression. Four out of eight items contained 80% of information for the given range of disabilities. This study has illustrated that IRT modeling is specifically suitable for accurate quantification of disease status and description and prediction of disease progression in phase 3 studies on RRMS, by integrating EDSS item-level data in a meaningful manner.

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“…Item Response Theory (IRT) has been reported to be a promising approach compared to the classical methods (5) in the development and validation of tests in patient-reported outcomes (PRO) research (6,7). It has also been extensively used in computerized adaptive educational testing applications (8).…”

Section: Introductionmentioning

confidence: 99%

Modeling a Composite Score in Parkinson’s Disease Using Item Response Theory

Gottipati

Karlsson

Plan

2017

AAPS J

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Abstract.In the current work, we present the methodology for development of an Item Response Theory model within a non-linear mixed effects framework to characterize the longitudinal changes of the Movement Disorder Society (sponsored revision) of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) endpoint in Parkinson's disease (PD). The data were obtained from Parkinson's Progression Markers Initiative database and included 163,070 observations up to 48 months from 430 subjects belonging to De Novo PD cohort. The probability of obtaining a score, reported for each of the items in the questionnaire, was modeled as a function of the subject's disability. Initially, a single latent variable model was explored to characterize the disease progression over time. However, based on the understanding of the questionnaire set-up and the results of a residuals-based diagnostic tool, a three latent variable model with a mixture implementation was able to adequately describe longitudinal changes not only at the total score level but also at each individual item level. The linear progression rates obtained for the patient-reported items and the non-sided items were similar, each of which roughly take about 50 months for a typical subject to progress linearly from the baseline by one standard deviation. However for the sided items, it was found that the better side deteriorates quicker than the disabled side. This study presents a framework for analyzing MDS-UPDRS data, which can be adapted to more traditional UPDRS data collected in PD clinical trials and result in more efficient designs and analyses of such studies.

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Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement

Abstract: When used appropriately, IRT can be a powerful tool for questionnaire development, evaluation, and refinement, resulting in precise, valid, and relatively brief instruments that minimize response burden.

Cited by 630 publications

References 51 publications

Establishing a Common Metric for Physical Function: Linking the HAQ-DI and SF-36 PF Subscale to PROMIS® Physical Function

Establishing a Common Metric for Physical Function: Linking the HAQ-DI and SF-36 PF Subscale to PROMIS® Physical Function

Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis

Modeling a Composite Score in Parkinson’s Disease Using Item Response Theory

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