Bayesian modelling for binary outcomes in the regression discontinuity design
Bayesian modelling for binary outcomes in the regression discontinuity design
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DOI：
10.1111/rssa.12440
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发表时间：
2019-06-01
影响因子：
2
通讯作者：
Baio, Gianluca
中科院分区：
文献类型：
--
作者：
Geneletti, Sara;Ricciardi, Federico;Baio, Gianluca
回归不连续性（RD）设计是一种准实验设计，它通过利用根据连续变量分配治疗的情况来模拟随机研究，这在许多药物治疗指南中很常见。研发设计文献主要关注持续结局。我们利用RD设计和工具变量之间的联系，以获得估计的因果风险比治疗时，结果是二进制的。有时，这个风险比的处理估计可以给负的置信下限。在贝叶斯框架中，我们施加了先验约束，以防止这种情况发生。这是新颖的，在频率论框架中不能轻易复制。我们比较我们的估计与估计方程和广义矩量法的基础上。在广泛的模拟的基础上，我们的方法相比，这两种方法，我们将我们的方法应用到一个真实的例子来估计他汀类药物的影响的概率低密度脂蛋白胆固醇水平达到推荐的水平。
The regression discontinuity (RD) design is a quasi-experimental design which emulates a randomized study by exploiting situations where treatment is assigned according to a continuous variable as is common in many drug treatment guidelines. The RD design literature focuses principally on continuous outcomes. We exploit the link between the RD design and instrumental variables to obtain an estimate for the causal risk ratio for the treated when the outcome is binary. Occasionally this risk ratio for the treated estimator can give negative lower confidence bounds. In the Bayesian framework we impose prior constraints that prevent this from happening. This is novel and cannot be easily reproduced in a frequentist framework. We compare our estimators with those based on estimating equation and generalized methods-of-moments methods. On the basis of extensive simulations our methods compare favourably with both methods and we apply our method to a real example to estimate the effect of statins on the probability of low density lipoprotein cholesterol levels reaching recommended levels.