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 - 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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U2 - 10.3389/fphar.2021.700776
DO - 10.3389/fphar.2021.700776
M3 - Article
C2 - 34393782
AN - SCOPUS:85112063684
SN - 1663-9812
VL - 12
JO - Frontiers in Pharmacology
JF - Frontiers in Pharmacology
M1 - 700776
ER -