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.

中文翻译：

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疾病潜伏期偏差的因果图


背景 疾病潜伏期定义为从疾病发生到疾病诊断的时间。在检查潜在结果的流行病学研究中可能会出现疾病潜伏偏差 (DLB)，因为疾病发生的确切时间未知，并且可能发生在暴露开始之前，可能导致偏差。尽管DLB 可以影响检查不同类型慢性疾病（例如阿尔茨海默病、癌症等）的流行病学研究，但DLB 向这些研究引入偏差的方式此前尚未阐明。有关 DLB 继发性偏差的具体类型及其结构的信息对于研究人员来说至关重要，以便更好地理解和控制 DLB。开发 在这里，我们描述了 DLB 可以使用有向无环图 (DAG) 将偏差（通过不同结构）引入解决潜在结果的流行病学研究的四种场景。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。应用 使用因果图，我们表明疾病潜伏期偏差可以通过以下方式影响流行病学研究的结果：（i）无法测量的混杂因素； (ii) 反向因果关系； (iii) 选择偏差； (iv) 通过调解人产生偏见。结论 疾病潜伏期偏差是一种重要的偏差，可能影响许多涉及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。