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
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医生处方偏好工具变量方法的评估：以二型糖尿病治疗为例
- 研究问题
- 医生处方偏好难以直接观测时，如何构造其工具变量替代指标，以及医生样本量、协变量缺失和偏好变化怎样影响效果？
- 方法与思路
- 作者用模拟比较规则型与模型型偏好指标，提出处理医生间差异及偏好变化的新方法，并以两种糖尿病治疗作案例。
- 材料陈述的贡献
- 摘要称指标构造应随数据条件选择；在处方数据充足、偏好变化且协变量非可忽略缺失的研究设置中，新方法可获得无偏估计。
- 阅读关注点
- 应核对处方偏好工具的有效性条件、替代指标构造与缺失机制，并检查无偏结论依赖哪些模拟设定及每位医生的数据量。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明工具有效性的具体依据和无偏结论的完整条件，不能保证跨场景成立。
原文摘要
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.