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J Rheumatol. 2007 Jun;34(6):1426-31.

Improving patient reported outcomes using item response theory and computerized adaptive testing.

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  • 1Division of Immunology and Rheumatology, Department of Medicine, Stanford University School of Medicine, Palo Alto, California, USA.

Abstract

OBJECTIVE:

Patient reported outcomes (PRO) are considered central outcome measures for both clinical trials and observational studies in rheumatology. More sophisticated statistical models, including item response theory (IRT) and computerized adaptive testing (CAT), will enable critical evaluation and reconstruction of currently utilized PRO instruments to improve measurement precision while reducing item burden on the individual patient.

METHODS:

We developed a domain hierarchy encompassing the latent trait of physical function/disability from the more general to most specific. Items collected from 165 English-language instruments were evaluated by a structured process including trained raters, modified Delphi expert consensus, and then patient evaluation. Each item in the refined data bank will undergo extensive analysis using IRT to evaluate response functions and measurement precision. CAT will allow for real-time questionnaires of potentially smaller numbers of questions tailored directly to each individual's level of physical function.

RESULTS:

Physical function/disability domain comprises 4 subdomains: upper extremity, trunk, lower extremity, and complex activities. Expert and patient review led to consensus favoring use of present-tense "capability" questions using a 4- or 5-item Likert response construct over past-tense "performance"items. Floor and ceiling effects, attribution of disability, and standardization of response categories were also addressed.

CONCLUSION:

By applying statistical techniques of IRT through use of CAT, existing PRO instruments may be improved to reduce questionnaire burden on the individual patients while increasing measurement precision that may ultimately lead to reduced sample size requirements for costly clinical trials.

PMID:
17552069
[PubMed - indexed for MEDLINE]
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