Noninterventional studies in the COVID-19 era: methodological considerations for study design and analysis

• Published in: Journal of clinical epidemiology 2023-01, Vol.153, p.91-101
• Main Authors: Butler, Anne M., Burcu, Mehmet, Christian, Jennifer B., Tian, Fang, Andersen, Kathleen M., Blumentals, William A., Joynt Maddox, Karen E., Alexander, G. Caleb
• Format: Article
• Language: English
• Subjects: Antibiotics; Bias; COVID-19; COVID-19 - epidemiology; Data analysis; Design; Design analysis; Electronic devices; Electronic health records; Electronic medical records; Emergency medical care; Emergency medical services; Epidemiology; Health services utilization; Hospitalization; Humans; Medicaid; Medical research; Methodology; Minority & ethnic groups; Missing data; Morbidity; Pandemics; Patients; Poverty; Prescription drugs; Real-world data; Real-world evidence; Research Design; Rural populations; Study design; Telemedicine; Urinary tract infections; Validity
• ISSN: 0895-4356; 1878-5921
• DOI: 10.1016/j.jclinepi.2022.11.011

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.