正如医生所嘱：观察性研究中基于医疗服务提供者偏好的工具变量方法评估，及其在2型糖尿病治疗比较效果中的应用
Just what the doctor ordered: An evaluation of provider preference-based Instrumental Variable methods in observational studies, with application for comparative effectiveness of type 2 diabetes therapy
AI总结：
本文通过模拟与2型糖尿病治疗案例，评估并提出基于医疗服务提供者处方偏好的工具变量构建方法，以适应不同数据条件并实现无偏因果效应估计。
摘要：
在使用观察性数据估计治疗效果时，工具变量为处理由未测量混杂导致的偏倚提供了一种方法。作为工具，个体医疗服务提供者的处方偏好已被提出。由于处方偏好难以测量且通常未被观测，分析时往往需要利用可得数据构建一个替代测量。这种替代测量可以采用不同的构建方法，例如利用观察到的治疗模式的简单基于规则的方法，或更复杂的基于模型的方法；后者采用正式统计模型解释治疗行为，同时考虑已测量的混杂因素。构建方法的选择取决于医疗服务提供者内部的数据可得性、已测量混杂因素中的缺失数据，以及处方偏好随时间可能发生的变化等方面。本文开展了一项全面的模拟研究，在不同数据条件下评估处方偏好替代变量的各种构建方法，这些条件包括：不同的医疗服务提供者规模、协变量数据缺失以及偏好变化。我们还提出了一种新的基于模型的构建方法，以处理医疗服务提供者之间的差异和处方偏好的变化。所有介绍的构建方法均在一项案例研究中得到示例说明，该研究使用观察性数据分析两种2型糖尿病治疗的相对降糖效果。研究表明，基于偏好的工具变量方法可以成为从观察性健康数据中进行因果推断的有用工具。构建方法的选择应由当前的数据条件决定。在每个医疗服务提供者具有充足处方数据、处方偏好随时间变化以及已测量混杂因素存在不可忽略缺失的情况下，我们提出的方法能够无偏地估计因果治疗效果。
英文摘要：
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.
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