PDF(Pubmed)
Abstract：
BACKGROUND: Observational studies are frequently used to estimate the comparative effectiveness of different colorectal cancer (CRC) screening methods due to the practical limitations and time needed to conduct large clinical trials. However, time-varying confounders, e.g. polyp detection in the last screening, can bias statistical results. Recently, generalized methods, or G-methods, have been used for the analysis of observational studies of CRC screening, given their ability to account for such time-varying confounders. Discretization, or the process of converting continuous functions into discrete counterparts, is required for G-methods when the treatment and outcomes are assessed at a continuous scale.
METHODS: This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency.
CONCLUSIONS: First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations.
CONCLUSIONS: The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs.
METHODS: This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency.
CONCLUSIONS: First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations.
CONCLUSIONS: The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs.
摘要：
背景：由于实际局限性和进行大型临床试验所需的时间，经常使用观察性研究来评估不同结直肠癌（CRC）筛查方法的相对有效性。然而,时变混杂因素，例如，在最后一次筛查中检测到息肉，会对统计结果产生偏差。最近,广义方法，或G-方法，已用于分析CRC筛查的观察性研究，考虑到他们解释这种时变混杂因素的能力。离散化，或者将连续函数转换为离散对应函数的过程，当连续评估治疗和结果时，G方法是必需的。
方法：本文评估了时变混杂和离散化之间的相互作用，这可能会导致评估筛查有效性的偏差。我们在评估不同的CRC筛查方法的效果时研究了这种偏倚，这些方法在典型的筛查频率上彼此不同。
结论：首先，用理论，我们确定了偏差的方向。然后，我们使用假设设置的模拟来研究不同离散化水平的偏差大小，筛查频率和研究周期的长度。我们开发了一种方法来评估在模拟情况下由于粗化而可能产生的偏差。
结论：所提出的方法可以为未来的筛查有效性研究提供信息，特别是对于CRC，通过确定数据离散化的间隔长度的选择，以最大程度地减少由于粗化而导致的偏差，同时平衡计算成本。
方法：本文评估了时变混杂和离散化之间的相互作用，这可能会导致评估筛查有效性的偏差。我们在评估不同的CRC筛查方法的效果时研究了这种偏倚，这些方法在典型的筛查频率上彼此不同。
结论：首先，用理论，我们确定了偏差的方向。然后，我们使用假设设置的模拟来研究不同离散化水平的偏差大小，筛查频率和研究周期的长度。我们开发了一种方法来评估在模拟情况下由于粗化而可能产生的偏差。
结论：所提出的方法可以为未来的筛查有效性研究提供信息，特别是对于CRC，通过确定数据离散化的间隔长度的选择，以最大程度地减少由于粗化而导致的偏差，同时平衡计算成本。
