TY - JOUR
T1 - Noninterventional studies in the COVID-19 era
T2 - methodological considerations for study design and analysis
AU - Butler, Anne M.
AU - Burcu, Mehmet
AU - Christian, Jennifer B.
AU - Tian, Fang
AU - Andersen, Kathleen M.
AU - Blumentals, William A.
AU - Joynt Maddox, Karen E.
AU - Alexander, G. Caleb
N1 - Funding Information:
Funding source: No specific funding for this work was provided. Dr Butler was supported by a grant from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (KL2 TR002346). Dr Andersen received doctoral training support from the National Heart, Lung and Blood Institute Pharmacoepidemiology T32 Training Program (T32HL139426-03).
Funding Information:
Disclosures: After the completion of this work, Dr Andersen became a full-time employee of Pfizer Inc. Dr Alexander is prior Chair and a current member of the FDA Peripheral and Central Nervous System Advisory Committee; is a consultant and holds equity in Monument Analytics, a health care consultancy whose clients include the life sciences industry as well as plaintiffs in opioid litigation; and is a prior member of OptumRx's National P&T Committee. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict of interest policies. Dr. Butler receives investigator-initiated funding from Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA. Dr. Burcu is an employee of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA and owns stock in Merck & Co., Inc., Rahway, NJ, USA. Dr Christian is an employee of and owns stock at IQVIA. Dr Blumentals is an employee of and owns stock in Sanofi stock. Dr Joynt Maddox serves on the Health Policy Advisory Committee for Centene Corp and has received research funding from Humana. Dr Tian is an employee of the U.S. Food and Drug Administration. The article reflects the views and opinions of the authors and should not be construed to represent the views and opinions of the U.S. Food and Drug Administration. This manuscript was endorsed by the International Society for Pharmacoepidemiology (ISPE). Funding source: No specific funding for this work was provided. Dr Butler was supported by a grant from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (KL2 TR002346). Dr Andersen received doctoral training support from the National Heart, Lung and Blood Institute Pharmacoepidemiology T32 Training Program (T32HL139426-03).
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2023/1
Y1 - 2023/1
N2 - The global COVID-19 pandemic has generated enormous morbidity and mortality, as well as large health system disruptions including changes in use of prescription medications, outpatient encounters, emergency department admissions, and hospitalizations. These pandemic-related disruptions are reflected in real-world data derived from electronic medical records, administrative claims, disease or medication registries, and mobile devices. We discuss how pandemic-related disruptions in healthcare utilization may impact the conduct of noninterventional studies designed to characterize the utilization and estimate the effects of medical interventions on health-related outcomes. Using hypothetical studies, we highlight consequences that the pandemic may have on study design elements including participant selection and ascertainment of exposures, outcomes, and covariates. We discuss the implications of these pandemic-related disruptions on possible threats to external validity (participant selection) and internal validity (for example, confounding, selection bias, missing data bias). These concerns may be amplified in populations disproportionately impacted by COVID-19, such as racial/ethnic minorities, rural residents, or people experiencing poverty. We propose a general framework for researchers to carefully consider during the design and analysis of noninterventional studies that use real-world data from the COVID-19 era.
AB - The global COVID-19 pandemic has generated enormous morbidity and mortality, as well as large health system disruptions including changes in use of prescription medications, outpatient encounters, emergency department admissions, and hospitalizations. These pandemic-related disruptions are reflected in real-world data derived from electronic medical records, administrative claims, disease or medication registries, and mobile devices. We discuss how pandemic-related disruptions in healthcare utilization may impact the conduct of noninterventional studies designed to characterize the utilization and estimate the effects of medical interventions on health-related outcomes. Using hypothetical studies, we highlight consequences that the pandemic may have on study design elements including participant selection and ascertainment of exposures, outcomes, and covariates. We discuss the implications of these pandemic-related disruptions on possible threats to external validity (participant selection) and internal validity (for example, confounding, selection bias, missing data bias). These concerns may be amplified in populations disproportionately impacted by COVID-19, such as racial/ethnic minorities, rural residents, or people experiencing poverty. We propose a general framework for researchers to carefully consider during the design and analysis of noninterventional studies that use real-world data from the COVID-19 era.
KW - COVID-19
KW - Data analysis
KW - Methodology
KW - Real-world data
KW - Real-world evidence
KW - Study design
UR - http://www.scopus.com/inward/record.url?scp=85146906798&partnerID=8YFLogxK
U2 - 10.1016/j.jclinepi.2022.11.011
DO - 10.1016/j.jclinepi.2022.11.011
M3 - Article
C2 - 36400263
AN - SCOPUS:85146906798
SN - 0895-4356
VL - 153
SP - 91
EP - 101
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
ER -