Your browser doesn't support javascript.
ISPE-Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID-19: Opportunities and Trade-Offs.
Sarri, Grammati; Bennett, Dimitri; Debray, Thomas; Deruaz-Luyet, Anouk; Soriano Gabarró, Montse; Largent, Joan A; Li, Xiaojuan; Liu, Wei; Lund, Jennifer L; Moga, Daniela C; Gokhale, Mugdha; Rentsch, Christopher T; Wen, Xuerong; Yanover, Chen; Ye, Yizhou; Yun, Huifeng; Zullo, Andrew R; Lin, Kueiyu Joshua.
  • Sarri G; Visiting Lead Scientist, Cytel Inc., London, UK.
  • Bennett D; Takeda Global Evidence and Outcomes, Takeda Pharmaceuticals USA, Inc., Cambridge, Massachusetts, USA.
  • Debray T; Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Deruaz-Luyet A; Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
  • Soriano Gabarró M; Smart Data Analysis and Statistics, Utrecht, The Netherlands.
  • Largent JA; Global Epidemiology and Real-World Evidence CoE, Corporate Medical Affairs, Boehringer Ingelheim International GmbH, Ingelheim-am-Rhein, Germany.
  • Li X; Bayer Partnerships and Integrated Evidence Generation Office, Integrated Evidence Generation & Business Innovation, Medical Affairs & Pharmacovigilance, Bayer AG, Berlin, Germany.
  • Liu W; Real-World Solutions, IQVIA, Mission Viejo, California, USA.
  • Lund JL; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
  • Moga DC; Division of Epidemiology, Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
  • Gokhale M; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Rentsch CT; Department of Pharmacy Practice and Science, College of Pharmacy, University of Kentucky, Lexington, Kentucky, USA.
  • Wen X; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Yanover C; Department of Epidemiology, Merck, West Point, Pennsylvania, USA.
  • Ye Y; Faculty of Epidemiology and Population Health, Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
  • Yun H; Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
  • Zullo AR; Health Outcomes, Pharmacy Practice, College of Pharmacy, University of Rhode Island, Kinston, Rhode Island, USA.
  • Lin KJ; KI Research Institute, Kfar Malal, Israel.
Clin Pharmacol Ther ; 112(5): 990-999, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-1694806
ABSTRACT
As the scientific research community along with healthcare professionals and decision makers around the world fight tirelessly against the coronavirus disease 2019 (COVID-19) pandemic, the need for comparative effectiveness research (CER) on preventive and therapeutic interventions for COVID-19 is immense. Randomized controlled trials markedly under-represent the frail and complex patients seen in routine care, and they do not typically have data on long-term treatment effects. The increasing availability of electronic health records (EHRs) for clinical research offers the opportunity to generate timely real-world evidence reflective of routine care for optimal management of COVID-19. However, there are many potential threats to the validity of CER based on EHR data that are not originally generated for research purposes. To ensure unbiased and robust results, we need high-quality healthcare databases, rigorous study designs, and proper implementation of appropriate statistical methods. We aimed to describe opportunities and challenges in EHR-based CER for COVID-19-related questions and to introduce best practices in pharmacoepidemiology to minimize potential biases. We structured our discussion into the following topics (1) study population identification based on exposure status; (2) ascertainment of outcomes; (3) common biases and potential solutions; and (iv) data operational challenges specific to COVID-19 CER using EHRs. We provide structured guidance for the proper conduct and appraisal of drug and vaccine effectiveness and safety research using EHR data for the pandemic. This paper is endorsed by the International Society for Pharmacoepidemiology (ISPE).
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Comparative Effectiveness Research / COVID-19 Type of study: Experimental Studies / Prognostic study / Qualitative research / Randomized controlled trials Topics: Vaccines Limits: Humans Language: English Journal: Clin Pharmacol Ther Year: 2022 Document Type: Article Affiliation country: Cpt.2560

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Comparative Effectiveness Research / COVID-19 Type of study: Experimental Studies / Prognostic study / Qualitative research / Randomized controlled trials Topics: Vaccines Limits: Humans Language: English Journal: Clin Pharmacol Ther Year: 2022 Document Type: Article Affiliation country: Cpt.2560