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Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes

BACKGROUND: Using a real dataset, we highlighted several major methodological issues raised by the estimation of the Minimal Clinically Important Difference (MCID) of a Patient-Reported Outcomes instrument. We especially considered the management of missing data and the use of more than two times of...

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Autores principales: Woaye-Hune, Pascal, Hardouin, Jean-Benoit, Lehur, Paul-Antoine, Meurette, Guillaume, Vanier, Antoine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251729/
https://www.ncbi.nlm.nih.gov/pubmed/32460882
http://dx.doi.org/10.1186/s12955-020-01398-w
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author Woaye-Hune, Pascal
Hardouin, Jean-Benoit
Lehur, Paul-Antoine
Meurette, Guillaume
Vanier, Antoine
author_facet Woaye-Hune, Pascal
Hardouin, Jean-Benoit
Lehur, Paul-Antoine
Meurette, Guillaume
Vanier, Antoine
author_sort Woaye-Hune, Pascal
collection PubMed
description BACKGROUND: Using a real dataset, we highlighted several major methodological issues raised by the estimation of the Minimal Clinically Important Difference (MCID) of a Patient-Reported Outcomes instrument. We especially considered the management of missing data and the use of more than two times of measurement. While inappropriate missing data management and inappropriate use of multiple time points can lead to loss of precision and/or bias in MCID estimation, these issues are almost never dealt with and require cautious considerations in the context of MCID estimation. METHODS: We used the LIGALONGO study (French Randomized Controlled Trial). We estimated MCID on the SF-36 General Health score by comparing many methods (distribution or anchor-based). Different techniques for imputation of missing data were performed (simple and multiple imputations). We also consider all measurement occasions by longitudinal modeling, and the dependence of the score difference on baseline. RESULTS: Three hundred ninety-three patients were studied. With distribution-based methods, a great variability in MCID was observed (from 3 to 26 points for improvement). Only 0.2 SD and 1/3 SD distribution methods gave MCID values consistent with anchor-based methods (from 4 to 7 points for improvement). The choice of missing data imputation technique clearly had an impact on MCID estimates. Simple imputation by mean score seemed to lead to out-of-range estimate, but as missing not at random mechanism can be hypothesized, even multiple imputations techniques can have led to an slight underestimation of MCID. Using 3 measurement occasions for improvement led to an increase in precision but lowered estimates. CONCLUSION: This practical example illustrates the substantial impact of some methodological issues that are usually never dealt with for MCID estimation. Simulation studies are needed to investigate those issues. TRIAL REGISTRATION: NCT01240772 (ClinicalTrials.gov) registered on November 15, 2010.
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spelling pubmed-72517292020-06-04 Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes Woaye-Hune, Pascal Hardouin, Jean-Benoit Lehur, Paul-Antoine Meurette, Guillaume Vanier, Antoine Health Qual Life Outcomes Research BACKGROUND: Using a real dataset, we highlighted several major methodological issues raised by the estimation of the Minimal Clinically Important Difference (MCID) of a Patient-Reported Outcomes instrument. We especially considered the management of missing data and the use of more than two times of measurement. While inappropriate missing data management and inappropriate use of multiple time points can lead to loss of precision and/or bias in MCID estimation, these issues are almost never dealt with and require cautious considerations in the context of MCID estimation. METHODS: We used the LIGALONGO study (French Randomized Controlled Trial). We estimated MCID on the SF-36 General Health score by comparing many methods (distribution or anchor-based). Different techniques for imputation of missing data were performed (simple and multiple imputations). We also consider all measurement occasions by longitudinal modeling, and the dependence of the score difference on baseline. RESULTS: Three hundred ninety-three patients were studied. With distribution-based methods, a great variability in MCID was observed (from 3 to 26 points for improvement). Only 0.2 SD and 1/3 SD distribution methods gave MCID values consistent with anchor-based methods (from 4 to 7 points for improvement). The choice of missing data imputation technique clearly had an impact on MCID estimates. Simple imputation by mean score seemed to lead to out-of-range estimate, but as missing not at random mechanism can be hypothesized, even multiple imputations techniques can have led to an slight underestimation of MCID. Using 3 measurement occasions for improvement led to an increase in precision but lowered estimates. CONCLUSION: This practical example illustrates the substantial impact of some methodological issues that are usually never dealt with for MCID estimation. Simulation studies are needed to investigate those issues. TRIAL REGISTRATION: NCT01240772 (ClinicalTrials.gov) registered on November 15, 2010. BioMed Central 2020-05-27 /pmc/articles/PMC7251729/ /pubmed/32460882 http://dx.doi.org/10.1186/s12955-020-01398-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Woaye-Hune, Pascal
Hardouin, Jean-Benoit
Lehur, Paul-Antoine
Meurette, Guillaume
Vanier, Antoine
Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes
title Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes
title_full Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes
title_fullStr Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes
title_full_unstemmed Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes
title_short Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes
title_sort practical issues encountered while determining minimal clinically important difference in patient-reported outcomes
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251729/
https://www.ncbi.nlm.nih.gov/pubmed/32460882
http://dx.doi.org/10.1186/s12955-020-01398-w
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