Barberio Julie, Ahern Thomas P, MacLehose Richard F, Collin Lindsay J, Cronin-Fenton Deirdre P, Damkier Per, Sørensen Henrik Toft, Lash Timothy L
Rollins School of Public Health, Emory University, Atlanta, GA, USA.
The Robert Larner College of Medicine, University of Vermont, Burlington, VT, USA.
Clin Epidemiol. 2021 Jul 27;13:627-635. doi: 10.2147/CLEP.S313613. eCollection 2021.
To compare the magnitude of bias due to unmeasured confounding estimated from various techniques in an applied example.
We examined the association between dibutyl phthalate (DBP) and incident estrogen receptor (ER)-positive breast cancer in a Danish nationwide cohort (N=1,122,042). Cox regression analyses were adjusted for age and active drug compounds contributing to DBP exposure. We estimated the hazard ratios (HRs) that would have been observed had one of the DBP sources been unmeasured and calculated the strength of confounding by comparing to the fully adjusted HR. We performed a quantitative bias analysis (QBA) of the "unmeasured" confounder, using external information to specify the bias parameters. Upper bounds on the bias were estimated and E-values were calculated.
The adjusted HR for incident ER-positive breast cancer among women with high-level (≥10,000 cumulative milligrams) versus no DBP exposure was 2.12 (95% confidence interval 1.12 to 4.05). Removing each DBP source in isolation resulted in negligible change in the HR. The bias estimates from the QBA ranged from 1.00 to 1.01. The estimated maximum impact of unmeasured confounding ranged from 1.01 to 1.51. E-values ranged from 3.46 to 3.68.
The impact of bias due to simulated unmeasured confounding was negligible, in part, because the unmeasured variable was not independent of controlled variables. When a suspected confounder cannot be measured in the study data, our exercise suggests that QBA is the most informative method for assessing the impact. E-values may best be reserved for situations where uncontrolled confounding emanates from an unknown confounder.
在一个实际应用示例中比较各种技术估计的未测量混杂因素导致的偏倚程度。
我们在丹麦全国队列（N = 1,122,042）中研究了邻苯二甲酸二丁酯（DBP）与雌激素受体（ER）阳性乳腺癌发病之间的关联。Cox回归分析对年龄和导致DBP暴露的活性药物成分进行了调整。我们估计了若其中一种DBP来源未被测量时本应观察到的风险比（HR），并通过与完全调整后的HR比较来计算混杂强度。我们对“未测量”的混杂因素进行了定量偏倚分析（QBA），利用外部信息确定偏倚参数。估计了偏倚的上限并计算了E值。
高水平（≥10,000累积毫克）暴露与无DBP暴露的女性中，ER阳性乳腺癌发病的调整后HR为2.12（95%置信区间1.12至4.05）。单独去除每种DBP来源导致HR变化可忽略不计。QBA得出的偏倚估计值范围为1.00至1.01。未测量混杂因素的估计最大影响范围为1.01至1.51。E值范围为3.46至3.68。
模拟的未测量混杂因素导致的偏倚影响可忽略不计，部分原因是未测量变量与已控制变量并非独立。当研究数据中无法测量可疑混杂因素时，我们的研究表明QBA是评估影响的最具信息量的方法。E值可能最适用于未控制的混杂因素源于未知混杂因素的情况。
