TY - JOUR
T1 - Ten Rules for Conducting Retrospective Pharmacoepidemiological Analyses
T2 - Example COVID-19 Study
AU - Powell, Michael
AU - Koenecke, Allison
AU - Byrd, James Brian
AU - Nishimura, Akihiko
AU - Konig, Maximilian F.
AU - Xiong, Ruoxuan
AU - Mahmood, Sadiqa
AU - Mucaj, Vera
AU - Bettegowda, Chetan
AU - Rose, Liam
AU - Tamang, Suzanne
AU - Sacarny, Adam
AU - Caffo, Brian
AU - Athey, Susan
AU - Stuart, Elizabeth A.
AU - Vogelstein, Joshua T.
N1 - Funding Information:
The contents of these 10 rules benefited from the support and feedback of a broad community, to include Evidence Accelerator, Cerner, and Observational Health Data Sciences and Informatics (OHDSI). We thank Elizabeth Ogburn, Henrik T. Sorensen, Todd Wagner, Jason LaBonte, Marc Succhard, and Sascha Dublin for many helpful discussions. We thank Julia Kuhl for producing the figures.
Funding Information:
Research was partially supported by funding from Microsoft Research and Fast Grants, part of the Emergent Ventures Program at The Mercatus Center at George Mason University. AK was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE–1656518. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. JB was supported by NIH K23HL128909 and FastGrants. MK was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of
Publisher Copyright:
© Copyright © 2021 Powell, Koenecke, Byrd, Nishimura, Konig, Xiong, Mahmood, Mucaj, Bettegowda, Rose, Tamang, Sacarny, Caffo, Athey, Stuart and Vogelstein.
PY - 2021/7/28
Y1 - 2021/7/28
N2 - Since the beginning of the COVID-19 pandemic, pharmaceutical treatment hypotheses have abounded, each requiring careful evaluation. A randomized controlled trial generally provides the most credible evaluation of a treatment, but the efficiency and effectiveness of the trial depend on the existing evidence supporting the treatment. The researcher must therefore compile a body of evidence justifying the use of time and resources to further investigate a treatment hypothesis in a trial. An observational study can provide this evidence, but the lack of randomized exposure and the researcher’s inability to control treatment administration and data collection introduce significant challenges. A proper analysis of observational health care data thus requires contributions from experts in a diverse set of topics ranging from epidemiology and causal analysis to relevant medical specialties and data sources. Here we summarize these contributions as 10 rules that serve as an end-to-end introduction to retrospective pharmacoepidemiological analyses of observational health care data using a running example of a hypothetical COVID-19 study. A detailed supplement presents a practical how-to guide for following each rule. When carefully designed and properly executed, a retrospective pharmacoepidemiological analysis framed around these rules will inform the decisions of whether and how to investigate a treatment hypothesis in a randomized controlled trial. This work has important implications for any future pandemic by prescribing what we can and should do while the world waits for global vaccine distribution.
AB - Since the beginning of the COVID-19 pandemic, pharmaceutical treatment hypotheses have abounded, each requiring careful evaluation. A randomized controlled trial generally provides the most credible evaluation of a treatment, but the efficiency and effectiveness of the trial depend on the existing evidence supporting the treatment. The researcher must therefore compile a body of evidence justifying the use of time and resources to further investigate a treatment hypothesis in a trial. An observational study can provide this evidence, but the lack of randomized exposure and the researcher’s inability to control treatment administration and data collection introduce significant challenges. A proper analysis of observational health care data thus requires contributions from experts in a diverse set of topics ranging from epidemiology and causal analysis to relevant medical specialties and data sources. Here we summarize these contributions as 10 rules that serve as an end-to-end introduction to retrospective pharmacoepidemiological analyses of observational health care data using a running example of a hypothetical COVID-19 study. A detailed supplement presents a practical how-to guide for following each rule. When carefully designed and properly executed, a retrospective pharmacoepidemiological analysis framed around these rules will inform the decisions of whether and how to investigate a treatment hypothesis in a randomized controlled trial. This work has important implications for any future pandemic by prescribing what we can and should do while the world waits for global vaccine distribution.
KW - COVID-19
KW - drug repurposing
KW - observational study
KW - pharmacoepidemiology
KW - retrospective analyses
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UR - http://www.scopus.com/inward/citedby.url?scp=85112063684&partnerID=8YFLogxK
U2 - 10.3389/fphar.2021.700776
DO - 10.3389/fphar.2021.700776
M3 - Article
C2 - 34393782
AN - SCOPUS:85112063684
VL - 12
JO - Frontiers in Pharmacology
JF - Frontiers in Pharmacology
SN - 1663-9812
M1 - 700776
ER -