1 Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, USA.
2 Department of Epidemiology, University of Washington, USA.
Stat Methods Med Res. 2018 Aug;27(8):2279-2293. doi: 10.1177/0962280216680240. Epub 2016 Dec 22.
In the modern era, cardiovascular biomarkers are often measured in the presence of medication use, such that the observed biomarker value for the treated participants is different than their underlying natural history value. However, for certain predictors (e.g. age, gender, and genetic exposures) the observed biomarker value is not of primary interest. Rather, we are interested in estimating the association between these predictors and the natural history of the biomarker that would have occurred in the absence of treatment. Nonrandom medication use obscures our ability to estimate this association in cross-sectional observational data. Structural equation methodology (e.g. the treatment effects model), while historically used to estimate treatment effects, has been previously shown to be a reasonable way to correct endogeneity bias when estimating natural biomarker associations. However, the assumption that the effects of medication use on the biomarker are uniform across participants on medication is generally not thought to be reasonable. We derive an extension of the treatment effects model to accommodate effect modification. Based on several simulation studies and an application to data from the Multi-Ethnic Study of Atherosclerosis, we show that our extension substantially improves bias in estimating associations of interest, particularly when effect modifiers are associated with the biomarker or with medication use, without a meaningful cost of efficiency.
在现代，心血管生物标志物通常在用药的情况下进行测量，因此治疗组参与者的观察到的生物标志物值与他们的潜在自然史值不同。然而，对于某些预测因子（例如年龄、性别和遗传暴露），观察到的生物标志物值并不是主要关注点。相反，我们有兴趣估计这些预测因子与生物标志物自然史之间的关联，而这种自然史是在没有治疗的情况下发生的。非随机用药会干扰我们在横断面观察性数据中估计这种关联的能力。结构方程方法（例如治疗效果模型）虽然历史上用于估计治疗效果，但之前已经表明，在估计自然生物标志物关联时，它是纠正内生性偏差的一种合理方法。然而，药物使用对生物标志物的影响在用药参与者中普遍一致的假设通常被认为是不合理的。我们推导出一种治疗效果模型的扩展，以适应效应修饰。基于几项模拟研究和对动脉粥样硬化多民族研究数据的应用，我们表明，我们的扩展大大改善了对感兴趣关联的估计偏差，特别是当效应修饰因子与生物标志物或与药物使用相关时，而不会显著降低效率。
