Collider bias undermines our understanding of COVID-19 disease risk and severity

TLDR

Observational COVID‑19 data are rapidly accumulating but are often drawn from non‑random samples such as hospital admissions or voluntary testing, creating bias that must be addressed through careful study design. The authors aim to highlight how collider bias complicates interpretation of such data and to propose tools and strategies—including a web app—to mitigate its effects. They illustrate collider bias using UK Biobank data, where COVID‑19 testing is highly selective for diverse genetic, behavioural, cardiovascular, demographic, and anthropometric traits, and discuss the sampling mechanisms that render aetiological studies vulnerable. They provide several mitigation tools and a web application for sensitivity analyses to help researchers assess and reduce collider bias in existing COVID‑19 studies.

Abstract

Abstract Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly, often from non-random sampling such as hospital admissions, targeted testing or voluntary participation. Here, we highlight the challenge of interpreting observational evidence from such samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.