COSMIN Risk of Bias checklist for systematic reviews of Patient-Reported Outcome Measures

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

Purpose

The original COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist was developed to assess the methodological quality of single studies on measurement properties of Patient-Reported Outcome Measures (PROMs). Now it is our aim to adapt the COSMIN checklist and its four-point rating system into a version exclusively for use in systematic reviews of PROMs, aiming to assess risk of bias of studies on measurement properties.

Methods

For each standard (i.e., a design requirement or preferred statistical method), it was discussed within the COSMIN steering committee if and how it should be adapted. The adapted checklist was pilot-tested to strengthen content validity in a systematic review on the quality of PROMs for patients with hand osteoarthritis.

Results

Most important changes were the reordering of the measurement properties to be assessed in a systematic review of PROMs; the deletion of standards that concerned reporting issues and standards that not necessarily lead to biased results; the integration of standards on general requirements for studies on item response theory with standards for specific measurement properties; the recommendation to the review team to specify hypotheses for construct validity and responsiveness in advance, and subsequently the removal of the standards about formulating hypotheses; and the change in the labels of the four-point rating system.

Conclusions

The COSMIN Risk of Bias checklist was developed exclusively for use in systematic reviews of PROMs to distinguish this application from other purposes of assessing the methodological quality of studies on measurement properties, such as guidance for designing or reporting a study on the measurement properties.

Electronic supplementary material

The online version of this article (10.1007/s11136-017-1765-4) contains supplementary material, which is available to authorized users.

Related collections

Most cited references 3

Record: found
Abstract: found
Article: not found

Development of EMPRO: a tool for the standardized assessment of patient-reported outcome measures.

José M. Valderas, Montse Ferrer, Joan Mendívil … (2015)

This study was aimed to develop a tool for the standardized assessment of patient-reported outcomes (PROs) to assist the choice of instruments. An expert panel adapted the eight attributes proposed by the Medical Outcomes Trust as evaluation review criteria, created items to evaluate them, and included a response scale for each item. A pilot test was designed to test the new tool's feasibility and to obtain preliminary information concerning its psychometric properties. The Spanish versions of five measures were selected for assessment: the SF-36 Health Survey, the Nottingham Health Profile, the COOP-WONCA charts, the EuroQol-5D, and the Quality of Life Questionnaire EORTC-QLQ-C30. We assessed the new tool's reliability (Cronbach's alpha and intraclass correlation coefficient [ICC]) and construct validity. The new EMPRO (Evaluating the Measurement of Patient-Reported Outcomes) tool has 39 items covering eight key attributes: conceptual and measurement model, reliability, validity, responsiveness, interpretability, burden, alternative modes of administration, and cross-cultural and linguistic adaptations. Internal consistency was high (alpha = 0.95) as was interrater concordance (ICC: 0.87-0.94). Positive associations consistent with a priori hypotheses were observed between EMPRO attribute scores and the number of articles identified for the measures, the years elapsed since the publication of the first article, and the number of citations. A new tool for the standardized assessment of PRO measures is available. It has shown good preliminary reliability and validity and should be a useful aid to investigators who need to choose between alternative measures. Further assessment of the tool is necessary.

0 comments Cited 75 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Causal indicators in quality of life research.

P Fayers, D. Hand, K. Bjordal … (1997)

Quality of Life (QOL) questionnaires contain two different types of items. Some items, such as assessments of symptoms of disease, may be called causal indicators because the occurrence of these symptoms can cause a change in QOL. A severe state of even a single symptom may suffice to cause impairment of QOL, although a poor QOL need not necessarily imply that a patient suffers from all the symptoms. Other items, for example anxiety and depression, can be regarded as effect indicators which reflect the level of QOL. These indicators usually have a more uniform relationship with QOL, and therefore a patient with poor QOL is likely to have low scores on all effect indicators. In extreme cases it may seem intuitively obvious which items are causal and which are effect indicators, but often it is less clear. We propose a model which includes these two types of indicators and show that they behave in markedly different ways. Formal quantitative methods are developed for distinguishing them. We also discuss the impact of this distinction upon instrument validation and the design and analysis of summary subscales.

0 comments Cited 37 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Checklist to operationalize measurement characteristics of patient-reported outcome measures

David Francis, Melissa L McPheeters, Meaghan Noud … (2016)

Background The purpose of this study was to advance a checklist of evaluative criteria designed to assess patient-reported outcome (PRO) measures’ developmental measurement properties and applicability, which can be used by systematic reviewers, researchers, and clinicians with a varied range of expertise in psychometric measure development methodology. Methods A directed literature search was performed to identify original studies, textbooks, consensus guidelines, and published reports that propose criteria for assessing the quality of PRO measures. Recommendations from these sources were iteratively distilled into a checklist of key attributes. Preliminary items underwent evaluation through 24 cognitive interviews with clinicians and quantitative researchers. Six measurement theory methodological novices independently applied the final checklist to assess six PRO measures encompassing a variety of methods, applications, and clinical constructs. Agreement between novice and expert scores was assessed. Results The distillation process yielded an 18-item checklist with six domains: (1) conceptual model, (2) content validity, (3) reliability, (4) construct validity, (5) scoring and interpretation, and (6) respondent burden and presentation. With minimal instruction, good agreement in checklist item ratings was achieved between quantitative researchers with expertise in measurement theory and less experienced clinicians (mean kappa 0.70; range 0.66–0.87). Conclusions We present a simplified checklist that can help guide systematic reviewers, researchers, and clinicians with varied measurement theory expertise to evaluate the strengths and weakness of candidate PRO measures’ developmental properties and the appropriateness for specific applications. Electronic supplementary material The online version of this article (doi:10.1186/s13643-016-0307-4) contains supplementary material, which is available to authorized users.

0 comments Cited 36 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

L. B. Mokkink:

ORCID: http://orcid.org/0000-0001-6489-2827

w.mokkink@vumc.nl

Journal

Journal ID (nlm-ta): Qual Life Res

Journal ID (iso-abbrev): Qual Life Res

Title: Quality of Life Research

Publisher: Springer International Publishing (Cham )

ISSN (Print): 0962-9343

ISSN (Electronic): 1573-2649

Publication date (Electronic): 19 December 2017

Publication date PMC-release: 19 December 2017

Publication date (Print): 2018

Volume: 27

Issue: 5

Pages: 1171-1179

Affiliations

[1 ]ISNI 0000 0004 0435 165X, GRID grid.16872.3a, Department of Epidemiology and Biostatistics and Amsterdam Public Health Research Institute, , VU University Medical Center, ; P.O. Box 7057, 1007 Amsterdam, The Netherlands

[2 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Health Services, , University of Washington, ; Seattle, WA USA

[3 ]ISNI 0000 0001 2172 2676, GRID grid.5612.0, Health Services Research Unit, , Institut Municipal d’Investigacio Medica (IMIM-Hospital del Mar), ; Barcelona, Spain

[4 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Department of Philosophy, Faculty of Humanities, , Vrije Universiteit, ; Amsterdam, The Netherlands

Author information

L. B. Mokkink http://orcid.org/0000-0001-6489-2827

Article

Publisher ID: 1765

DOI: 10.1007/s11136-017-1765-4

PMC ID: 5891552

PubMed ID: 29260445

SO-VID: 1c8776cb-c9cf-48e9-916e-3180d73be9b8

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

History

Date accepted : 12 December 2017

Custom metadata

ScienceOpen disciplines: Public health

Keywords: quality assessment,systematic review,risk of bias,measurement properties,outcome measurement instruments

Data availability:

ScienceOpen disciplines: Public health

Keywords: quality assessment, systematic review, risk of bias, measurement properties, outcome measurement instruments

Comments

Comment on this article

scite_

Smart Citations

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.

COSMIN Risk of Bias checklist for systematic reviews of Patient-Reported Outcome Measures

Read this article at

Abstract

Purpose

Methods

Results

Conclusions

Electronic supplementary material

Related collections

Global Health Next Generation Network

Most cited references 3

Development of EMPRO: a tool for the standardized assessment of patient-reported outcome measures.

Causal indicators in quality of life research.

Checklist to operationalize measurement characteristics of patient-reported outcome measures

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Categories

Custom metadata

Comments

Comment on this article

Similar content 326

Cited by 735

Most referenced authors 456