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Recommendations for the use of propensity score methods in multiple sclerosis research
BACKGROUND: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in mul...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9260471/ https://www.ncbi.nlm.nih.gov/pubmed/35387508 http://dx.doi.org/10.1177/13524585221085733 |
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author | Simoneau, Gabrielle Pellegrini, Fabio Debray, Thomas PA Rouette, Julie Muñoz, Johanna Platt, Robert W. Petkau, John Bohn, Justin Shen, Changyu de Moor, Carl Karim, Mohammad Ehsanul |
author_facet | Simoneau, Gabrielle Pellegrini, Fabio Debray, Thomas PA Rouette, Julie Muñoz, Johanna Platt, Robert W. Petkau, John Bohn, Justin Shen, Changyu de Moor, Carl Karim, Mohammad Ehsanul |
author_sort | Simoneau, Gabrielle |
collection | PubMed |
description | BACKGROUND: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies. OBJECTIVES: To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research. METHODS: We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice. RESULTS: Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses. CONCLUSION: This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods. |
format | Online Article Text |
id | pubmed-9260471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-92604712022-07-08 Recommendations for the use of propensity score methods in multiple sclerosis research Simoneau, Gabrielle Pellegrini, Fabio Debray, Thomas PA Rouette, Julie Muñoz, Johanna Platt, Robert W. Petkau, John Bohn, Justin Shen, Changyu de Moor, Carl Karim, Mohammad Ehsanul Mult Scler Future Perspectives BACKGROUND: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies. OBJECTIVES: To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research. METHODS: We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice. RESULTS: Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses. CONCLUSION: This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods. SAGE Publications 2022-04-06 2022-08 /pmc/articles/PMC9260471/ /pubmed/35387508 http://dx.doi.org/10.1177/13524585221085733 Text en © The Author(s), 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Future Perspectives Simoneau, Gabrielle Pellegrini, Fabio Debray, Thomas PA Rouette, Julie Muñoz, Johanna Platt, Robert W. Petkau, John Bohn, Justin Shen, Changyu de Moor, Carl Karim, Mohammad Ehsanul Recommendations for the use of propensity score methods in multiple sclerosis research |
title | Recommendations for the use of propensity score methods in multiple
sclerosis research |
title_full | Recommendations for the use of propensity score methods in multiple
sclerosis research |
title_fullStr | Recommendations for the use of propensity score methods in multiple
sclerosis research |
title_full_unstemmed | Recommendations for the use of propensity score methods in multiple
sclerosis research |
title_short | Recommendations for the use of propensity score methods in multiple
sclerosis research |
title_sort | recommendations for the use of propensity score methods in multiple
sclerosis research |
topic | Future Perspectives |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9260471/ https://www.ncbi.nlm.nih.gov/pubmed/35387508 http://dx.doi.org/10.1177/13524585221085733 |
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