Instrumental Variables provide a way of addressing bias due to unmeasured confounding when estimating treatment effects using observational data. As instrument prescription preference of individual healthcare providers has been proposed. Because prescription preference is hard to measure and often unobserved, a surrogate measure constructed from available data is often required for the analysis. Different construction methods for this surrogate measure are possible, such as simple rule-based methods which make use of the observed treatment patterns, or more complex model-based methods that employ formal statistical models to explain the treatment behaviour whilst considering measured confounders. The choice of construction method relies on aspects like data availability within provider, missing data in measured confounders, and possible changes in prescription preference over time. In this paper we conduct a comprehensive simulation study to evaluate different construction methods for surrogates of prescription preference under different data conditions, including: different provider sizes, missing covariate data, and change in preference. We also propose a novel model-based construction method to address between provider differences and change in prescription preference. All presented construction methods are exemplified in a case study of the relative glucose lowering effect of two type 2 diabetes treatments in observational data. Our study shows that preference-based Instrumental Variable methods can be a useful tool for causal inference from observational health data. The choice of construction method should be driven by the data condition at hand. Our proposed method is capable of estimating the causal treatment effect without bias in case of sufficient prescription data per provider, changing prescription preference over time and non-ignorable missingness in measured confounders.

翻译：工具变量法为解决观察性数据估计处理效应时因未测量混杂因素导致的偏倚提供了途径。其中，个体医疗服务提供者的处方偏好被提出作为工具变量。由于处方偏好难以测量且通常未被观测，分析中需利用现有数据构建替代指标。替代指标的构建方法多种多样，例如基于观察治疗模式的简单规则法，或采用正式统计模型解释治疗行为同时考虑已测量混杂因素的复杂模型法。构建方法的选择取决于提供者内部数据可用性、已测量混杂因素的缺失数据以及处方偏好随时间变化等因素。本文通过全面的模拟研究，评估了不同数据条件下（包括不同提供者规模、协变量缺失数据及偏好变化）处方偏好替代指标的不同构建方法。同时，我们提出了一种新型基于模型的构建方法，以处理提供者间差异及处方偏好变化。所有构建方法均通过观察性数据中两种2型糖尿病治疗方案的降糖效果比较案例研究加以说明。研究表明，基于偏好的工具变量方法可成为观察性健康数据因果推断的有效工具。构建方法的选择应根据具体数据条件决定。当每个提供者具有充足处方数据、处方偏好随时间变化且已测量混杂变量存在非可忽略缺失时，本文提出的方法能够无偏估计因果处理效应。