疾病潜伏期偏差的因果图示分析
Causal diagrams for disease latency bias
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影响因子:5.9
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
DOI:
10.1093/ije/dyae111
摘要
疾病潜伏期定义为从疾病发生到诊断的时间。疾病潜伏期偏差（DLB）可能在流行病学研究中出现，特别是在研究潜伏性疾病结局时，由于疾病的确切起始时间未知，且可能在暴露开始之前就已发生，可能导致偏差。尽管DLB会影响研究不同类型慢性疾病（如阿尔茨海默病、癌症等）的流行病学研究，但关于DLB如何引入偏差的机制尚未被充分阐明。了解DLB可能引发的具体偏差类型及其结构对于研究人员来说至关重要，以便更好地理解和控制DLB。本研究通过有向非循环图（DAGs）描述了四种DLB可能引入偏差的情境（通过不同的结构），并讨论了理解、检验和控制DLB的潜在策略。利用因果图，我们指出疾病潜伏期偏差可能通过以下途径影响流行病学研究结果： (i) 未测量的混杂因素； (ii) 反向因果关系； (iii) 选择偏差； (iv) 通过中介变量引起的偏差。疾病潜伏期偏差是影响多项潜伏性结局流行病学研究的重要偏差，因果图能帮助研究者更有效地识别和控制这一偏差。
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.