纵向数据中内源性药物使用的生物标志物年龄趋势的半参数估计
Semi-parametric estimation of biomarker age trends with endogenous medication use in longitudinal data
摘要：
在队列研究中，非随机药物使用可能对估计平均生物标志物值的自然史趋势（即，感兴趣的预测因子与在没有生物标志物特异性治疗的情况下观察到的生物标志物结果之间的关联）构成障碍。治疗和结果的常见原因往往是不可测量的，模糊了我们的能力，很容易解释药物使用与常用的假设，如可重复性。此外，如果没有一些变量满足排除限制，使用工具变量的方法可能难以证明。具有结构转移的Heckman混合模型（有时不太具体地称为处理效应模型）可用于通过同质性假设（即，平均处理效应在协变量之间没有变化）和用于结果和处理的联合模型的参数规格。在最近的工作中，我们放宽了同质性假设，允许观察到的协变量作为处理效应修饰符。虽然这种方法已被证明是合理的横截面数据的设置，该方法的应用程序设置的纵向数据仍然没有探索。我们演示了如何假设的处理效应模型可以扩展到适应集群数据所产生的纵向研究。我们提出的方法是半参数的性质，有效的推断，可以得到，而不需要指定的纵向相关结构。作为一个说明性的例子，我们使用多种族动脉粥样硬化研究的数据来评估低密度脂蛋白的趋势，按年龄和性别。我们证实，我们的处理效应模型的泛化可以作为一个有用的工具，揭示自然历史趋势的纵向数据被掩盖的内源性治疗。
英文摘要：
In cohort studies, non-random medication use can pose barriers to estimation of the natural history trend in a mean biomarker value (namely, the association between a predictor of interest and a biomarker outcome that would be observed in the absence of biomarker-specific treatment). Common causes of treatment and outcomes are often unmeasured, obscuring our ability to easily account for medication use with commonly invoked assumptions such as ignorability. Further, absent some variable satisfying the exclusion restriction, use of instrumental variable approaches may be difficult to justify. Heckman's hybrid model with structural shift (sometimes referred to less specifically as the treatment effects model) can be used to correct endogeneity bias via a homogeneity assumption (i.e., that average treatment effects do not vary across covariates) and parametric specification of a joint model for the outcome and treatment. In recent work, we relaxed the homogeneity assumption by allowing observed covariates to serve as treatment effect modifiers. While this method has been shown to be reasonably robust in settings of cross-sectional data, application of this methodology to settings of longitudinal data remains unexplored. We demonstrate how the assumptions of the treatment effects model can be extended to accommodate clustered data arising from longitudinal studies. Our proposed approach is semi-parametric in nature in that valid inference can be obtained without the need to specify the longitudinal correlation structure. As an illustrative example, we use data from the Multi-Ethnic Study of Atherosclerosis to evaluate trends in low-density lipoprotein by age and gender. We confirm that our generalization of the treatment effects model can serve as a useful tool to uncover natural history trends in longitudinal data that are obscured by endogenous treatment.
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