Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data.
Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data.
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DOI：
10.1002/sim.6486
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发表时间：
2015-07-10
影响因子：
2
通讯作者：
Baio G
中科院分区：
文献类型：
--
作者：
Geneletti S;O'Keeffe AG;Sharples LD;Richardson S;Baio G
回归不连续性（RD）设计是一种准实验设计，通过利用自然发生的处理规则来估计处理的因果效应。它可以应用于根据与连续变量相关的预定规则进行特定治疗或干预的任何情况。此类阈值在初级保健药物处方中很常见，其中研发设计可用于估计一般人群中药物的因果效应。然后，这些结果可以与随机对照试验（RCT）中获得的结果进行对比，并基于更现实和更便宜的背景为处方政策和指南提供信息。在本文中，我们重点关注他汀类药物，这是一类降胆固醇药物，然而，该方法可以应用于许多其他药物，前提是这些药物是根据预定的指南开的。英国目前的指南规定，应将他汀类药物处方给10年心血管疾病风险评分超过20%的患者。如果我们考虑风险评分接近20%风险评分阈值的患者，我们发现风险评分本身及其测量值都存在随机变化。因此，我们可以将阈值视为一种随机化装置，将他汀类药物处方分配给略高于阈值的个体，并将其从略低于阈值的个体中扣除。因此，我们在阈值附近的区域有效地复制了RCT的条件，消除或至少减轻了混淆。我们的RD设计框架的语言的条件独立性，澄清了必要的假设，应用RD设计的数据，并使工具变量的联系清晰。我们还具有关于他汀类药物处方的预期效应大小的特定背景知识，因此能够通过在因果参数上制定信息先验来将其纳入贝叶斯模型。© 2015作者。出版社：John Wiley & Sons Ltd
The regression discontinuity (RD) design is a quasi‐experimental design that estimates the causal effects of a treatment by exploiting naturally occurring treatment rules. It can be applied in any context where a particular treatment or intervention is administered according to a pre‐specified rule linked to a continuous variable. Such thresholds are common in primary care drug prescription where the RD design can be used to estimate the causal effect of medication in the general population. Such results can then be contrasted to those obtained from randomised controlled trials (RCTs) and inform prescription policy and guidelines based on a more realistic and less expensive context. In this paper, we focus on statins, a class of cholesterol‐lowering drugs, however, the methodology can be applied to many other drugs provided these are prescribed in accordance to pre‐determined guidelines. Current guidelines in the UK state that statins should be prescribed to patients with 10‐year cardiovascular disease risk scores in excess of 20%. If we consider patients whose risk scores are close to the 20% risk score threshold, we find that there is an element of random variation in both the risk score itself and its measurement. We can therefore consider the threshold as a randomising device that assigns statin prescription to individuals just above the threshold and withholds it from those just below. Thus, we are effectively replicating the conditions of an RCT in the area around the threshold, removing or at least mitigating confounding. We frame the RD design in the language of conditional independence, which clarifies the assumptions necessary to apply an RD design to data, and which makes the links with instrumental variables clear. We also have context‐specific knowledge about the expected sizes of the effects of statin prescription and are thus able to incorporate this into Bayesian models by formulating informative priors on our causal parameters. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.