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Methods for shortening patient-reported outcome measures.

Patient-reported outcome measures are widely used to assess patient experiences, well-being, and treatment response in clinical trials and cohort-based observational studies. However, patients may be asked to respond to many different measures in order to provide researchers and clinicians with a wi...

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Published in:Statistical Methods in Medical Research Vol. 28; no. 10/11; pp. 2992 - 3012
Main Authors: Harel, Daphna, Baron, Murray
Format: equations & formulas research tables/charts Journal Article
Published: Sage Publications Inc. Oct/Nov2019
Online Access:View this record in EBSCOhost
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      dt: Oct/Nov2019
      vid: 28
      iid: 10/11
      pid: 344
      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: Methods for shortening patient-reported outcome measures.
      aug:
        au:
          Harel, Daphna
          Baron, Murray
        affil: Department of Applied Statistics, Social Science, and Humanities, New York University, New York, NY, USA
      sug:
        subj:
          Models, Statistical
          Scleroderma, Systemic Physiopathology
          Human
          Fatigue Physiopathology
          Computer Simulation
          Reproducibility of Results
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
      ab: Patient-reported outcome measures are widely used to assess patient experiences, well-being, and treatment response in clinical trials and cohort-based observational studies. However, patients may be asked to respond to many different measures in order to provide researchers and clinicians with a wide array of information regarding their experiences. Collecting such long and cumbersome patient-reported outcome measures may burden patients, increase research costs, and potentially reduce the quality of the data collected. Nonetheless, little research has been conducted on replicable, and reproducible methods to shorten these instruments that result in shortened forms of minimal length. This manuscript proposes the use of mixed integer programming through Optimal Test Assembly as a method to shorten patient-reported outcome measures. This method is compared to the existing standard in the field, which is selecting items based on having high discrimination parameters from an item response theory model. The method is then illustrated in an application to a fatigue scale for patients with Systemic Sclerosis.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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