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When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?
The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real‐world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9291272/ https://www.ncbi.nlm.nih.gov/pubmed/33826756 http://dx.doi.org/10.1002/cpt.2255 |
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author | Franklin, Jessica M. Platt, Richard Dreyer, Nancy A. London, Alex John Simon, Gregory E. Watanabe, Jonathan H. Horberg, Michael Hernandez, Adrian Califf, Robert M. |
author_facet | Franklin, Jessica M. Platt, Richard Dreyer, Nancy A. London, Alex John Simon, Gregory E. Watanabe, Jonathan H. Horberg, Michael Hernandez, Adrian Califf, Robert M. |
author_sort | Franklin, Jessica M. |
collection | PubMed |
description | The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real‐world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design‐related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high‐quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions. |
format | Online Article Text |
id | pubmed-9291272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92912722022-07-20 When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments? Franklin, Jessica M. Platt, Richard Dreyer, Nancy A. London, Alex John Simon, Gregory E. Watanabe, Jonathan H. Horberg, Michael Hernandez, Adrian Califf, Robert M. Clin Pharmacol Ther Reviews The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real‐world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design‐related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high‐quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions. John Wiley and Sons Inc. 2021-05-09 2022-01 /pmc/articles/PMC9291272/ /pubmed/33826756 http://dx.doi.org/10.1002/cpt.2255 Text en © 2021 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Reviews Franklin, Jessica M. Platt, Richard Dreyer, Nancy A. London, Alex John Simon, Gregory E. Watanabe, Jonathan H. Horberg, Michael Hernandez, Adrian Califf, Robert M. When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments? |
title | When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments? |
title_full | When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments? |
title_fullStr | When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments? |
title_full_unstemmed | When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments? |
title_short | When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments? |
title_sort | when can nonrandomized studies support valid inference regarding effectiveness or safety of new medical treatments? |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9291272/ https://www.ncbi.nlm.nih.gov/pubmed/33826756 http://dx.doi.org/10.1002/cpt.2255 |
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