JOURNAL ARTICLE

Causal diagrams for disease latency bias.

  • Published In: International Journal of Epidemiology, 2024, v. 53, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Etminan, Mahyar; Rezaeianzadeh, Ramin; Mansournia, Mohammad A 3 of 3

Abstract

The article focuses on disease latency bias (DLB), a type of bias in epidemiological studies of latent outcomes where the time from disease initiation to diagnosis is unknown, potentially leading to distorted results. Using directed acyclic graphs (DAGs), the authors illustrate four mechanisms by which DLB can introduce bias: unmeasured confounding, reverse causality, selection bias, and mediation bias. They emphasize that DLB arises because exposure is often measured after symptom onset but before diagnosis, complicating causal inference in diseases with long latency periods such as Alzheimer's disease. The article suggests that DAGs can help researchers identify and control for DLB, and discusses strategies including statistical adjustment, lag periods, and probabilistic bias analysis to mitigate its effects.

Additional Information

  • Source:International Journal of Epidemiology. 2024/10, Vol. 53, Issue 5, p1
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2024
  • ISSN:0300-5771
  • DOI:10.1093/ije/dyae111
  • Accession Number:180217970
  • Copyright Statement:Copyright of International Journal of Epidemiology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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