How is the minimal clinically important difference established in health-related quality of life instruments? Review of anchors and methods
| Autor: | Elisabeth Jouve, Christel Castelli, Yosra Mouelhi, Stéphanie Gentile |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male medicine.medical_specialty Psychometrics Computer science Distribution-based methods Context (language use) Review lcsh:Computer applications to medicine. Medical informatics Standard deviation Quality of life Anchors-based methods medicine Humans Medical physics Aged Quality of Life Research Health related quality of life Minimal clinically important difference Public Health Environmental and Occupational Health General Medicine Middle Aged humanities Health-related-quality of life Standard error Quality of Life lcsh:R858-859.7 Female |
| Zdroj: | Health and Quality of Life Outcomes Health and Quality of Life Outcomes, Vol 18, Iss 1, Pp 1-17 (2020) |
| ISSN: | 1477-7525 |
| DOI: | 10.1186/s12955-020-01344-w |
| Popis: | Background The aim of this systematic review is to describe the different types of anchors and statistical methods used in estimating the Minimal Clinically Important Difference (MCID) for Health-Related Quality of Life (HRQoL) instruments. Methods PubMed and Google scholar were searched for English and French language studies published from 2010 to 2018 using selected keywords. We included original articles (reviews, meta-analysis, commentaries and research letters were not considered) that described anchors and statistical methods used to estimate the MCID in HRQoL instruments. Results Forty-seven papers satisfied the inclusion criteria. The MCID was estimated for 6 generic and 18 disease-specific instruments. Most studies in our review used anchor-based methods (n = 41), either alone or in combination with distribution-based methods. The most common applied anchors were non-clinical, from the viewpoint of patients. Different statistical methods for anchor-based methods were applied and the Change Difference (CD) was the most used one. Most distributional methods included 0.2 standard deviations (SD), 0.3 SD, 0.5 SD and 1 standard error of measurement (SEM). MCID values were very variable depending on methods applied, and also on clinical context of the study. Conclusion Multiple anchors and methods were applied in the included studies, which lead to different estimations of MCID. Using several methods enables to assess the robustness of the results. This corresponds to a sensitivity analysis of the methods. Close collaboration between statisticians and clinicians is recommended to integrate an agreement regarding the appropriate method to determine MCID for a specific context. |
| Databáze: | OpenAIRE |
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