2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Comparative responsiveness of the health utilities index and the RAND-12 for multiple sclerosis

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background:

          Outcome measures need to be valid and have good test–retest reliability and responsiveness. We compared the responsiveness of the RAND-12 and the Health Utilities Index—mark III (HUI3) in persons with multiple sclerosis (MS).

          Methods:

          In Spring 2018 and 2019, North American Research Committee on Multiple Sclerosis (NARCOMS) registry participants completed the HUI3, the RAND-12, and reported disability (Patient Determined Disease Steps (PDDS)) and employment status (full-time, part-time, and no). We used changes in PDDS and employment status as anchors. We assessed responsiveness using effect size, standardized response mean, and the responsiveness index. We used relative efficiency (RE) to compare the responsiveness of the health-related quality of life (HRQOL) scores, adjusting for sociodemographic factors.

          Results:

          We included 4769 participants in the analysis. They had a mean (standard deviation (SD)) age of 60.9 (10.1) years, and 3826 participants (80.2%) were women. RE was highest for the HUI3 for changes in in disability status (HUI3: 1.0, Physical Component Score-12 (PCS-12): 0.80, and Mental Component Score-12 (MCS-12): 0.41) and for changes in employment status (HUI3: 1.0, PCS-12: 0.70, and MCS-12: 0.17).

          Conclusion:

          The HUI3 was more responsive to changes in disability and employment status than the PCS-12 or MCS-12. Given the HUI3’s other strong psychometric properties, it may be the preferred generic measure of HRQOL in MS.

          Related collections

          Most cited references18

          • Record: found
          • Abstract: found
          • Article: not found

          The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes.

          Lack of consensus on taxonomy, terminology, and definitions has led to confusion about which measurement properties are relevant and which concepts they represent. The aim was to clarify and standardize terminology and definitions of measurement properties by reaching consensus among a group of experts and to develop a taxonomy of measurement properties relevant for evaluating health instruments. An international Delphi study with four written rounds was performed. Participating experts had a background in epidemiology, statistics, psychology, and clinical medicine. The panel was asked to rate their (dis)agreement about proposals on a five-point scale. Consensus was considered to be reached when at least 67% of the panel agreed. Of 91 invited experts, 57 agreed to participate and 43 actually participated. Consensus was reached on positions of measurement properties in the taxonomy (68-84%), terminology (74-88%, except for structural validity [56%]), and definitions of measurement properties (68-88%). The panel extensively discussed the positions of internal consistency and responsiveness in the taxonomy, the terms "reliability" and "structural validity," and the definitions of internal consistency and reliability. Consensus on taxonomy, terminology, and definitions of measurement properties was reached. Hopefully, this will lead to a more uniform use of terms and definitions in the literature on measurement properties. Copyright 2010 Elsevier Inc. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes.

            The objective of this review is to summarize recommendations on methods for evaluating responsiveness and minimal important difference (MID) for patient-reported outcome (PRO) measures. We review, summarize, and integrate information on issues and methods for evaluating responsiveness and determining MID estimates for PRO measures. Recommendations are made on best-practice methods for evaluating responsiveness and MID. The MID for a PRO instrument is not an immutable characteristic, but may vary by population and context, and no one MID may be valid for all study applications. MID estimates should be based on multiple approaches and triangulation of methods. Anchor-based methods applying various relevant patient-rated, clinician-rated, and disease-specific variables provide primary and meaningful estimates of an instrument's MID. Results for the PRO measures from clinical trials can also provide insight into observed effects based on treatment comparisons and should be used to help determine MID. Distribution-based methods can support estimates from anchor-based approaches and can be used in situations where anchor-based estimates are unavailable. We recommend that the MID is based primarily on relevant patient-based and clinical anchors, with clinical trial experience used to further inform understanding of MID.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The RAND 36-Item Health Survey 1.0.

              Recently, Ware and Sherbourne published a new short-form health survey, the MOS 36-Item Short-Form Health Survey (SF-36), consisting of 36 items included in long-form measures developed for the Medical Outcomes Study. The SF-36 taps eight health concepts: physical functioning, bodily pain, role limitations due to physical health problems, role limitations due to personal or emotional problems, general mental health, social functioning, energy/fatigue, and general health perceptions. It also includes a single item that provides an indication of perceived change in health. The SF-36 items and scoring rules are distributed by MOS Trust, Inc. Strict adherence to item wording and scoring recommendations is required in order to use the SF-36 trademark. The RAND 36-Item Health Survey 1.0 (distributed by RAND) includes the same items as those in the SF-36, but the recommended scoring algorithm is somewhat different from that of the SF-36. Scoring differences are discussed here and new T-scores are presented for the 8 multi-item scales and two factor analytically-derived physical and mental health composite scores.
                Bookmark

                Author and article information

                Contributors
                Journal
                Multiple Sclerosis Journal
                Mult Scler
                SAGE Publications
                1352-4585
                1477-0970
                October 2021
                January 05 2021
                October 2021
                : 27
                : 11
                : 1781-1789
                Affiliations
                [1 ]Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Health Sciences Centre, Winnipeg, MB, Canada
                [2 ]Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
                [3 ]Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
                [4 ]Mellen Center for Multiple Sclerosis, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
                [5 ]Department of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
                Article
                10.1177/1352458520981370
                347afe4d-530b-4d6d-84d0-47da52d5c3e0
                © 2021

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

                History

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content193

                Cited by3

                Most referenced authors313