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

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Title: Enhancing Measurement Precision of Patient-Reported Outcomes Using Item Response Theory.
Authors: Ntumi, Simon1 (AUTHOR) sntumi@uew.edu.gh, Sakyi, Lawrence Larbi2 (AUTHOR), Bulala, Tapela3 (AUTHOR), Agbovor, Divine1 (AUTHOR), Amfo, Gabriel Odame1 (AUTHOR), Gabla, John1 (AUTHOR), Anakwa, Rudi1 (AUTHOR), Amezah, Emmanuel Ohene1 (AUTHOR)
Source: Inquiry (00469580). 4/13/2026, Vol. 63, p1-11. 11p.
Subject Terms: *Predictive tests, *Statistical correlation, *Self-evaluation, *Research methodology evaluation, *Quality of life, *Research, *Clinics, *Factor analysis, Statistical models, Clinical medicine, Cross-sectional method, Patient compliance, Scale analysis (Psychology), Health status indicators, Cronbach's alpha, Key performance indicators (Management), Health policy, Research evaluation, Statistical sampling, Questionnaires, Evaluation of medical care, Descriptive statistics, Quantitative research, Decision making in clinical medicine, Chi-squared test, Judgment sampling, Psychometrics, Health outcome assessment, Health equity, Data analysis software, Reliability (Personality trait)
Geographic Terms: Ghana
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. [ABSTRACT FROM AUTHOR]
Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/00469580261441163
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1
    Subjects:
      – SubjectFull: Predictive tests
        Type: general
      – SubjectFull: Statistical correlation
        Type: general
      – SubjectFull: Self-evaluation
        Type: general
      – SubjectFull: Research methodology evaluation
        Type: general
      – SubjectFull: Quality of life
        Type: general
      – SubjectFull: Research
        Type: general
      – SubjectFull: Clinics
        Type: general
      – SubjectFull: Factor analysis
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Clinical medicine
        Type: general
      – SubjectFull: Cross-sectional method
        Type: general
      – SubjectFull: Patient compliance
        Type: general
      – SubjectFull: Scale analysis (Psychology)
        Type: general
      – SubjectFull: Health status indicators
        Type: general
      – SubjectFull: Cronbach's alpha
        Type: general
      – SubjectFull: Key performance indicators (Management)
        Type: general
      – SubjectFull: Health policy
        Type: general
      – SubjectFull: Research evaluation
        Type: general
      – SubjectFull: Statistical sampling
        Type: general
      – SubjectFull: Questionnaires
        Type: general
      – SubjectFull: Evaluation of medical care
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Quantitative research
        Type: general
      – SubjectFull: Decision making in clinical medicine
        Type: general
      – SubjectFull: Chi-squared test
        Type: general
      – SubjectFull: Judgment sampling
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      – SubjectFull: Psychometrics
        Type: general
      – SubjectFull: Health outcome assessment
        Type: general
      – SubjectFull: Health equity
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Reliability (Personality trait)
        Type: general
      – SubjectFull: Ghana
        Type: general
    Titles:
      – TitleFull: Enhancing Measurement Precision of Patient-Reported Outcomes Using Item Response Theory.
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              M: 04
              Text: 4/13/2026
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              Y: 2026
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