Causal diagrams for disease latency bias

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 leadin...

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Vydáno v:International journal of epidemiology Ročník 53; číslo 5
Hlavní autoři: Etminan, Mahyar, Rezaeianzadeh, Ramin, Mansournia, Mohammad A
Médium: Journal Article
Jazyk:angličtina
Vydáno: England 14.08.2024
Témata:
Bias
Causality
Confounding Factors, Epidemiologic
Epidemiologic Studies
Humans
Selection Bias
confounder
selection bias
reverse causality bias
Disease latency bias
bias
causal diagrams
ISSN:1464-3685, 1464-3685
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Shrnutí: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. 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. 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. 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.
Bibliografie:ObjectType-Article-1
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ISSN:1464-3685
1464-3685
DOI:10.1093/ije/dyae111