Approaches to the Estimation of the Local Average Treatment Effect in a Regression Discontinuity Design.
Approaches to the Estimation of the Local Average Treatment Effect in a Regression Discontinuity Design.
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DOI：
10.1111/sjos.12224
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Baio G
中科院分区：
文献类型：
--
作者：
O'Keeffe AG;Baio G
不连续回归设计（RD设计）被用作从观察数据进行因果推断的方法，其中根据与某些连续变量相关的“决策规则”做出应用干预的决策。这种设计在医学上越来越多地得到发展。局部平均治疗效应（LATE）已被确定为RD设计中干预效应的估计值，特别是在未严格遵守设计“决策规则”的情况下。估计LATE的方差不一定是简单的。我们考虑三种方法来估计LATE：两阶段最小二乘，基于似然和贝叶斯方法。我们比较了各种模拟RD设计和一个真实的例子，关于处方他汀类药物的基础上，心血管疾病的风险评分。
Regression discontinuity designs (RD designs) are used as a method for causal inference from observational data, where the decision to apply an intervention is made according to a ‘decision rule’ that is linked to some continuous variable. Such designs are being increasingly developed in medicine. The local average treatment effect (LATE) has been established as an estimator of the intervention effect in an RD design, particularly where a design's ‘decision rule’ is not adhered to strictly. Estimating the variance of the LATE is not necessarily straightforward. We consider three approaches to the estimation of the LATE: two‐stage least squares, likelihood‐based and a Bayesian approach. We compare these under a variety of simulated RD designs and a real example concerning the prescription of statins based on cardiovascular disease risk score.
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