In epidemiology, left-truncated data may bias exposure effect estimates. We analyzed the bias induced by left truncation in estimating breast cancer risk associated with exposure to airborne dioxins. Simulations were run with exposure estimates from a Geographic Information System (GIS)-based metric and considered two hypotheses for historical exposure, three scenarios for intra-individual correlation of annual exposures, and three exposure-effect models. For each correlation/model combination, 500 nested matched case–control studies were simulated and data fitted using a conditional logistic regression model. Bias magnitude was assessed by estimated odds-ratios (ORs) versus theoretical relative risks (TRRs) comparisons. With strong intra-individual correlation and continuous exposure, left truncation overestimated the Beta parameter associated with cumulative dioxin exposure. Versus a theoretical Beta of 4.17, the estimated mean Beta (5%; 95%) was 73.2 (67.7; 78.8) with left-truncated exposure and 4.37 (4.05; 4.66) with lifetime exposure. With exposure categorized in quintiles, the TRR was 2.0, the estimated OR_{Q5 vs. Q1} 2.19 (2.04; 2.33) with truncated exposure versus 2.17 (2.02; 2.32) with lifetime exposure. However, the difference in exposure between Q5 and Q1 was 18× smaller with truncated data, indicating an important overestimation of the dose effect. No intra-individual correlation resulted in effect dilution and statistical power loss. Left truncation induced substantial bias in estimating breast cancer risk associated with exposure with continuous and categorical models. With strong intra-individual exposure correlation, both models detected associations, but categorical models provided better estimates of effect trends. This calls for careful consideration of left truncation-induced bias in interpreting environmental epidemiological data.

在流行病学中，左截断数据可能会使暴露效应估计产生偏差。我们分析了左截断在估计与暴露于空气中二恶英相关的乳腺癌风险时引起的偏差。使用基于地理信息系统 (GIS) 的指标的暴露估计值运行模拟，并考虑了历史暴露的两个假设、年度暴露的个人内部相关性的三个情景和三个暴露效应模型。对于每个相关/模型组合，模拟了 500 个嵌套匹配的病例对照研究，并使用条件逻辑回归模型拟合数据。通过估计优势比 (OR) 与理论相对风险 (TRR) 比较来评估偏差幅度。具有很强的个体内相关性和持续曝光，左截断高估了与累积二恶英暴露相关的 Beta 参数。与 4.17 的理论 Beta 相比，左截断暴露的估计平均 Beta（5%；95%）为 73.2（67.7；78.8），终生暴露为 4.37（4.05；4.66）。将暴露分类为五分位数，TRR 为 2.0，估计的 OR_{Q5 与 Q1} 2.19 (2.04; 2.33) 与 2.17 (2.02; 2.32) 与终生暴露相比。然而，在截断数据的情况下，Q5 和 Q1 之间的暴露差异小 18 倍，表明严重高估了剂量效应。没有个体内相关性导致效应稀释和统计功率损失。左截断在使用连续和分类模型估计与暴露相关的乳腺癌风险时引起了重大偏差。由于个体内暴露相关性强，两种模型都检测到了关联，但分类模型提供了对效果趋势的更好估计。这要求在解释环境流行病学数据时仔细考虑左截断引起的偏差。