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Exploring the impact of selection bias in observational studies of COVID-19: a simulation study.

Louise A C MillardAlba Fernández-SanlésAlice Rose CarterRachael A HughesKate TillingTim P MorrisDaniel Major-SmithGareth J GriffithGemma L ClaytonEmily KawabataGeorge Davey SmithDeborah A LawlorMaria Carolina Borges
Published in: International journal of epidemiology (2022)
Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depend on the outcome definition, the true effect of the risk factor and the assumed selection mechanism; these are likely to differ between studies with different target populations. Bias due to sample selection is a key concern in COVID-19 research based on national registry data, especially as countries end free mass testing. The framework we have used can be applied by other researchers assessing the extent to which their results may be biased for their research question of interest.
Keyphrases
  • coronavirus disease
  • sars cov
  • electronic health record
  • quality improvement
  • risk factors
  • big data
  • respiratory syndrome coronavirus
  • case control