Causal diagrams for disease latency bias.
| Autor: | Etminan M; Department of Ophthalmology and Visual Sciences and Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada., Rezaeianzadeh R; Department of Ophthalmology and Visual Sciences and Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada., Mansournia MA; Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. |
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| Jazyk: | angličtina |
| Zdroj: | International journal of epidemiology [Int J Epidemiol] 2024 Aug 14; Vol. 53 (5). |
| DOI: | 10.1093/ije/dyae111 |
| Abstrakt: | Background: Disease latency is defined as the time from disease initiation to disease diagnosis. Disease latency bias (DLB) can arise in epidemiological studies that examine latent outcomes, since the exact timing of the disease inception is unknown and might occur before exposure initiation, potentially leading to bias. Although DLB can affect epidemiological studies that examine different types of chronic disease (e.g. Alzheimer's disease, cancer etc), the manner by which DLB can introduce bias into these studies has not been previously elucidated. Information on the specific types of bias, and their structure, that can arise secondary to DLB is critical for researchers, to enable better understanding and control for DLB. Development: Here we describe four scenarios by which DLB can introduce bias (through different structures) into epidemiological studies that address latent outcomes, using directed acyclic graphs (DAGs). We also discuss potential strategies to better understand, examine and control for DLB in these studies. Application: Using causal diagrams, we show that disease latency bias can affect results of epidemiological studies through: (i) unmeasured confounding; (ii) reverse causality; (iii) selection bias; (iv) bias through a mediator. Conclusion: Disease latency bias is an important bias that can affect a number of epidemiological studies that address latent outcomes. Causal diagrams can assist researchers better identify and control for this bias. (© The Author(s) 2024; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.) |
| Databáze: | MEDLINE |
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