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Ten Rules for Conducting Retrospective Pharmacoepidemiological Analyses: Example COVID-19 Study.
Powell, Michael; Koenecke, Allison; Byrd, James Brian; Nishimura, Akihiko; Konig, Maximilian F; Xiong, Ruoxuan; Mahmood, Sadiqa; Mucaj, Vera; Bettegowda, Chetan; Rose, Liam; Tamang, Suzanne; Sacarny, Adam; Caffo, Brian; Athey, Susan; Stuart, Elizabeth A; Vogelstein, Joshua T.
Afiliación
  • Powell M; Department of Biomedical Engineering, Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, United States.
  • Koenecke A; Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA, United States.
  • Byrd JB; Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.
  • Nishimura A; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health at Johns Hopkins University, Baltimore, MD, United States.
  • Konig MF; Ludwig Center, Lustgarten Laboratory, Howard Hughes Medical Institute, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
  • Xiong R; Division of Rheumatology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
  • Mahmood S; Graduate School of Business, Stanford University, Stanford, CA, United States.
  • Mucaj V; Health Catalyst Inc., Salt Lake City, UT, United States.
  • Bettegowda C; Datavant Inc., San Francisco, CA, United States.
  • Rose L; Ludwig Center, Lustgarten Laboratory, Howard Hughes Medical Institute, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
  • Tamang S; Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
  • Sacarny A; VA Health Economics Resource Center, Palo Alto VA, Menlo Park, CA, United States.
  • Caffo B; Department of Biomedical Data Science, Stanford University, Stanford, CA, United States.
  • Athey S; Department of Health Policy and Management, Columbia University Mailman School of Public Health, New York, NY, United States.
  • Stuart EA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health at Johns Hopkins University, Baltimore, MD, United States.
  • Vogelstein JT; Graduate School of Business, Stanford University, Stanford, CA, United States.
Front Pharmacol ; 12: 700776, 2021.
Article en En | MEDLINE | ID: mdl-34393782
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Observational_studies Idioma: En Revista: Front Pharmacol Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Observational_studies Idioma: En Revista: Front Pharmacol Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza