论文标题
固定效果，混合效应和仪器变量模型的群体内部和集群间混淆的影响对效应估计器的偏差的影响
The Impact of Unmeasured Within- and Between-Cluster Confounding on the Bias of Effect Estimators from Fixed Effect, Mixed effect and Instrumental Variable Models
论文作者
论文摘要
仪器变量方法是打击未衡量的混杂以获得较少偏见效应估计值的流行选择。但是，我们证明，根据未衡量的混杂的性质，替代方法可能会产生较少的偏见估计。簇的治疗偏好（例如，医师实践）是仪器变量分析（IVA）中最常用的工具。这些基于偏好的IVA通常是在由区域，医院/设施或医师聚集的数据上进行的，在这些数据中，通常会在簇之间或之间的混杂混淆。我们旨在量化未衡量的混杂对IVA效应估计器偏差的影响，以及包括普通最小二乘回归，线性混合模型（LMM）和固定效应模型（FE）的替代方法，以研究连续暴露的影响（例如，治疗剂量）。在存在群体内和/或集群间混杂因子内部和/或群体之间，我们从这四种方法中得出估计量的偏差公式。我们表明，当存在群集中混淆时，IVA可以提供一致的估计，而当存在群体之间的混淆时，IVA可以提供一致的估计。另一方面，当群集间混淆出口之间，FES和LMM可以提供一致的估计，而不是用于集群内混淆。 IVA在减少FES和LMM的偏差方面是否有利取决于相对于群间混杂之间的混杂群体内部混杂的程度。此外，未衡量的混杂在IVA估计中的影响大于未衡量的集群内混淆对FE和LMM估计的影响。我们通过数据应用程序说明了这些方法。我们的发现为选择适当的方法来打击未衡量的混杂因素的主要类型的指导，并有助于解释统计结果。
Instrumental variable methods are popular choices in combating unmeasured confounding to obtain less biased effect estimates. However, we demonstrate that alternative methods may give less biased estimates depending on the nature of unmeasured confounding. Treatment preferences of clusters (e.g., physician practices) are the most f6requently used instruments in instrumental variable analyses (IVA). These preference-based IVAs are usually conducted on data clustered by region, hospital/facility, or physician, where unmeasured confounding often occurs within or between clusters. We aim to quantify the impact of unmeasured confounding on the bias of effect estimators in IVA, as well as alternative methods including ordinary least squares regression, linear mixed models (LMM) and fixed effect models (FE) to study the effect of a continuous exposure (e.g., treatment dose). We derive bias formulae of estimators from these four methods in the presence of unmeasured within- and/or between-cluster confounders. We show that IVAs can provide consistent estimates when unmeasured within-cluster confounding exists, but not when between-cluster confounding exists. On the other hand, FEs and LMMs can provide consistent estimates when unmeasured between-cluster confounding exits, but not for within-cluster confounding. Whether IVAs are advantageous in reducing bias over FEs and LMMs depends on the extent of unmeasured within-cluster confounding relative to between-cluster confounding. Furthermore, the impact of unmeasured between-cluster confounding on IVA estimates is larger than the impact of unmeasured within-cluster confounding on FE and LMM estimates. We illustrate these methods through data applications. Our findings provide guidance for choosing appropriate methods to combat the dominant types of unmeasured confounders and help interpret statistical results in the context of unmeasured confounding
