Abstract：
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.
METHODS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
METHODS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
摘要：
背景：疾病潜伏期定义为从疾病开始到疾病诊断的时间。疾病潜伏期偏倚（DLB）可能出现在流行病学研究，检查潜在的结果，由于疾病开始的确切时间是未知的，可能发生在暴露开始之前，可能导致偏见。虽然DLB可以影响流行病学研究，检查不同类型的慢性疾病（如阿尔茨海默病，癌症等)，以前尚未阐明DLB在这些研究中引入偏倚的方式.关于偏见的特定类型的信息，和它们的结构，这可能是DLB的次要原因对研究人员来说至关重要，以便更好地理解和控制DLB。
方法：在这里，我们描述了DLB可以将偏倚（通过不同的结构）引入流行病学研究以解决潜在结果的四种情况。使用有向无环图(DAG)。我们还讨论了潜在的策略，以更好地理解，在这些研究中检查和控制DLB。
结论：使用因果图，我们发现疾病潜伏期偏倚可以通过以下方式影响流行病学研究的结果:(i)未测量的混杂因素;(ii)反向因果关系;(iii)选择偏倚;(iv)介体偏倚.
结论：疾病潜伏期偏倚是一种重要的偏倚，可影响许多针对潜在结局的流行病学研究。因果图可以帮助研究人员更好地识别和控制这种偏见。
方法：在这里，我们描述了DLB可以将偏倚（通过不同的结构）引入流行病学研究以解决潜在结果的四种情况。使用有向无环图(DAG)。我们还讨论了潜在的策略，以更好地理解，在这些研究中检查和控制DLB。
结论：使用因果图，我们发现疾病潜伏期偏倚可以通过以下方式影响流行病学研究的结果:(i)未测量的混杂因素;(ii)反向因果关系;(iii)选择偏倚;(iv)介体偏倚.
结论：疾病潜伏期偏倚是一种重要的偏倚，可影响许多针对潜在结局的流行病学研究。因果图可以帮助研究人员更好地识别和控制这种偏见。
