疾病潜伏期偏差的因果图
Causal diagrams for disease latency bias
影响因子:5.90000
分区:医学2区 Top / 公共卫生1区
发表日期:2024 Aug 14
作者:
Mahyar Etminan, Ramin Rezaeianzadeh, Mohammad A Mansournia
摘要
疾病潜伏期定义为从疾病开始到疾病诊断的时间。疾病潜伏期偏见（DLB）可能在检查潜在结果的流行病学研究中会出现，因为该疾病的确切时机尚不清楚，并且可能发生在暴露启动之前，可能导致偏见。尽管DLB可能会影响研究不同类型的慢性疾病（例如阿尔茨海默氏病，癌症等）的流行病学研究，但DLB可以在这些研究中引入偏见的方式尚未阐明。关于DLB继发的特定类型及其结构的特定类型的信息对于研究人员至关重要，可以更好地理解和控制DLB。在这里，我们描述了DLB可以使用有指导的acyclic图（DAGS）来解决潜在的偏见，以解决DLB可以将偏见（通过不同的结构）引入潜在的偏见。我们还讨论了在这些研究中更好地理解，检查和控制DLB的潜在策略。使用因果图，我们表明疾病潜伏期偏见可以通过以下方式影响流行病学研究的结果：（i）未衡量的混杂； （ii）反向因果关系； （iii）选择偏见； （iv）通过调解人的偏见。disease延迟偏见是一种重要的偏见，可能影响许多解决潜在结果的流行病学研究。因果图可以帮助研究人员更好地识别和控制这种偏见。
Abstract
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.