Semi-parametric estimation of biomarker age trends with endogenous medication use in longitudinal data
内生用药下纵向生物标志物年龄趋势的半参数估计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究纵向聚类数据，并校正内生治疗以恢复未治疗状态下的结局趋势。
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内生用药下纵向生物标志物年龄趋势的半参数估计
- 研究问题
- 纵向队列中用药并非随机且存在未测混杂时，如何恢复未接受相关治疗情况下的生物标志物均值年龄趋势？
- 方法与思路
- 作者将允许观测协变量修饰治疗效应的结构位移治疗效应模型扩展至纵向聚类数据，推断无需指定纵向相关结构。
- 材料陈述的贡献
- 摘要称该半参数扩展能揭示被内生治疗遮蔽的自然病程趋势，并以多族裔动脉粥样硬化研究展示年龄和性别相关分析。
- 阅读关注点
- 应核对结局与治疗联合模型的识别限制、效应修饰设定、聚类推断的条件，以及免设相关结构与其他模型假设的边界。
- 阅读边界
- 所据为 arXiv 预印本，不能据此确认同行评审或期刊接收；无需指定相关结构不代表无需识别假设，摘要未列完整条件。
原文摘要
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.