Department of Public Health, North Dakota State University, Fargo, North Dakota, United States of America.
Division of Environmental Health Sciences, University of Minnesota School of Public Health, Minneapolis, Minnesota, United States of America.
PLoS One. 2018 Mar 19;13(3):e0194608. doi: 10.1371/journal.pone.0194608. eCollection 2018.
When some individuals are screen-detected before the beginning of the study, but otherwise would have been diagnosed symptomatically during the study, this results in different case-ascertainment probabilities among screened and unscreened participants, referred to here as lead-time-biased case-ascertainment (LTBCA). In fact, this issue can arise even in risk-factor studies nested within a randomized screening trial; even though the screening intervention is randomly allocated to trial arms, there is no randomization to potential risk-factors and uptake of screening can differ by risk-factor strata. Under the assumptions that neither screening nor the risk factor affects underlying incidence and no other forms of bias operate, we simulate and compare the underlying cumulative incidence and that observed in the study due to LTBCA. The example used will be constructed from the randomized Prostate, Lung, Colorectal, and Ovarian cancer screening trial. The derived mathematical model is applied to simulating two nested studies to evaluate the potential for screening bias in observational lung cancer studies. Because of differential screening under plausible assumptions about preclinical incidence and duration, the simulations presented here show that LTBCA due to chest x-ray screening can significantly increase the estimated risk of lung cancer due to smoking by 1% and 50%. Traditional adjustment methods cannot account for this bias, as the influence screening has on observational study estimates involves events outside of the study observation window (enrollment and follow-up) that change eligibility for potential participants, thus biasing case ascertainment.
当一些个体在研究开始前被筛查出来，但在研究期间本会因症状而被诊断，这会导致筛查组和未筛查组的病例检出概率不同，我们将其称为领先时间偏倚性病例检出（lead-time-biased case-ascertainment，LTBCA）。实际上，即使在嵌套于随机筛查试验的风险因素研究中，也可能出现这个问题；尽管筛查干预是随机分配到试验组的，但对潜在风险因素没有进行随机化，并且筛查的接受程度可能因风险因素分层而不同。在不存在筛查或风险因素会影响基础发病率且没有其他形式的偏倚的假设下，我们模拟并比较了由于 LTBCA 而导致的基础累积发病率和研究中观察到的发病率。所使用的示例将根据前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验的随机分组构建。所得到的数学模型用于模拟两个嵌套研究，以评估在观察性肺癌研究中筛查偏倚的可能性。由于在关于临床前发病率和持续时间的合理假设下存在差异筛查，因此这里呈现的模拟结果表明，由于 X 射线筛查导致的 LTBCA 可能会使因吸烟而导致的肺癌风险增加 1%和 50%。传统的调整方法无法解释这种偏倚，因为筛查对观察性研究估计值的影响涉及研究观察窗口（入组和随访）之外的事件，这些事件改变了潜在参与者的资格，从而偏倚了病例检出。
