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Evaluating and improving real-world evidence with Targeted Learning
BACKGROUND: The Targeted Learning roadmap provides a systematic guide for generating and evaluating real-world evidence (RWE). From a regulatory perspective, RWE arises from diverse sources such as randomized controlled trials that make use of real-world data, observational studies, and other study...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394864/ https://www.ncbi.nlm.nih.gov/pubmed/37533017 http://dx.doi.org/10.1186/s12874-023-01998-2 |
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author | Gruber, Susan Phillips, Rachael V. Lee, Hana Concato, John van der Laan, Mark |
author_facet | Gruber, Susan Phillips, Rachael V. Lee, Hana Concato, John van der Laan, Mark |
author_sort | Gruber, Susan |
collection | PubMed |
description | BACKGROUND: The Targeted Learning roadmap provides a systematic guide for generating and evaluating real-world evidence (RWE). From a regulatory perspective, RWE arises from diverse sources such as randomized controlled trials that make use of real-world data, observational studies, and other study designs. This paper illustrates a principled approach to assessing the validity and interpretability of RWE. METHODS: We applied the roadmap to a published observational study of the dose–response association between ritodrine hydrochloride and pulmonary edema among women pregnant with twins in Japan. The goal was to identify barriers to causal effect estimation beyond unmeasured confounding reported by the study's authors, and to explore potential options for overcoming the barriers that robustify results. RESULTS: Following the roadmap raised issues that led us to formulate alternative causal questions that produced more reliable, interpretable RWE. The process revealed a lack of information in the available data to identify a causal dose–response curve. However, under explicit assumptions the effect of treatment with any amount of ritodrine versus none, albeit a less ambitious parameter, can be estimated from data. CONCLUSIONS: Before RWE can be used in support of clinical and regulatory decision-making, its quality and reliability must be systematically evaluated. The TL roadmap prescribes how to carry out a thorough, transparent, and realistic assessment of RWE. We recommend this approach be a routine part of any decision-making process. |
format | Online Article Text |
id | pubmed-10394864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103948642023-08-03 Evaluating and improving real-world evidence with Targeted Learning Gruber, Susan Phillips, Rachael V. Lee, Hana Concato, John van der Laan, Mark BMC Med Res Methodol Research Article BACKGROUND: The Targeted Learning roadmap provides a systematic guide for generating and evaluating real-world evidence (RWE). From a regulatory perspective, RWE arises from diverse sources such as randomized controlled trials that make use of real-world data, observational studies, and other study designs. This paper illustrates a principled approach to assessing the validity and interpretability of RWE. METHODS: We applied the roadmap to a published observational study of the dose–response association between ritodrine hydrochloride and pulmonary edema among women pregnant with twins in Japan. The goal was to identify barriers to causal effect estimation beyond unmeasured confounding reported by the study's authors, and to explore potential options for overcoming the barriers that robustify results. RESULTS: Following the roadmap raised issues that led us to formulate alternative causal questions that produced more reliable, interpretable RWE. The process revealed a lack of information in the available data to identify a causal dose–response curve. However, under explicit assumptions the effect of treatment with any amount of ritodrine versus none, albeit a less ambitious parameter, can be estimated from data. CONCLUSIONS: Before RWE can be used in support of clinical and regulatory decision-making, its quality and reliability must be systematically evaluated. The TL roadmap prescribes how to carry out a thorough, transparent, and realistic assessment of RWE. We recommend this approach be a routine part of any decision-making process. BioMed Central 2023-08-02 /pmc/articles/PMC10394864/ /pubmed/37533017 http://dx.doi.org/10.1186/s12874-023-01998-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Article Gruber, Susan Phillips, Rachael V. Lee, Hana Concato, John van der Laan, Mark Evaluating and improving real-world evidence with Targeted Learning |
title | Evaluating and improving real-world evidence with Targeted Learning |
title_full | Evaluating and improving real-world evidence with Targeted Learning |
title_fullStr | Evaluating and improving real-world evidence with Targeted Learning |
title_full_unstemmed | Evaluating and improving real-world evidence with Targeted Learning |
title_short | Evaluating and improving real-world evidence with Targeted Learning |
title_sort | evaluating and improving real-world evidence with targeted learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394864/ https://www.ncbi.nlm.nih.gov/pubmed/37533017 http://dx.doi.org/10.1186/s12874-023-01998-2 |
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