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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...

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Autores principales: Franklin, Jessica M., Platt, Richard, Dreyer, Nancy A., London, Alex John, Simon, Gregory E., Watanabe, Jonathan H., Horberg, Michael, Hernandez, Adrian, Califf, Robert M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
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.
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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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