Avoiding bias in self‐controlled case series studies of coronavirus disease 2019

Fonseca‐Rodríguez, Osvaldo; Fors Connolly, Anne‐Marie; Katsoularis, Ioannis; Lindmark, Krister and Farrington, Paddy (2021). Avoiding bias in self‐controlled case series studies of coronavirus disease 2019. Statistics in Medicine, 40(27) pp. 6197–6208.

DOI: https://doi.org/10.1002/sim.9179

Abstract

Many studies, including self-controlled case series (SCCS) studies, are being undertaken to quantify the risks of complications following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19). One such SCCS study, based on all COVID-19 cases arising in Sweden over an 8-month period, has shown that SARS-CoV-2 infection increases the risks of AMI and ischemic stroke. Some features of SARS-CoV-2 infection and COVID-19, present in this study and likely in others, complicate the analysis and may introduce bias. In the present paper we describe these features, and explore the biases they may generate. Motivated by data-based simulations, we propose methods to reduce or remove these biases.

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• Item ORO ID
• 78958
• Item Type
• Journal Item
• ISSN
• 0277-6715
• Keywords
• bias; cardiovascular disease; COVID-19; epidemiological methods; mortality; self-controlled case series
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2021 John Wiley & Sons Ltd.
• Depositing User
• ORO Import