因时间区间粗化导致的偏倚：结直肠癌筛查效果推断中的影响
Bias due to coarsening of time intervals in the inference for the effectiveness of colorectal cancer screening
DOI 原文链接
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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 Jun 12
作者:
Bikram Karmakar, Ann G Zauber, Anne I Hahn, Yan Kwan Lau, Chyke A Doubeni, Marshall M Joffe
DOI:
10.1093/ije/dyae096
摘要
由于实际限制及大规模临床试验所需的时间，观察性研究常被用来估算不同结直肠癌（CRC）筛查方法的比较效果。然而，时间变化的混杂因素，例如上次筛查中息肉检测，可能会偏倚统计结果。近年来，为了考虑此类时间变化的混杂因素，已使用广义方法（G-方法）分析CRC筛查的观察性研究。将连续函数转化为离散对应物的过程——离散化，是G-方法在治疗和结果按连续尺度评估时的必要步骤。本研究评估了时间变化的混杂因素与离散化之间的相互作用，及其在评估筛查效果时可能引起的偏倚。我们探讨了不同筛查频率下，离散化水平对偏倚大小的影响。通过模拟假设场景，开发了评估由于离散化引起偏倚的方法。该方法可为未来CRC筛查效果研究提供指导，帮助选择合适的时间间隔，减少偏倚，同时平衡计算成本。
Abstract
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.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.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.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.