Regression Discontinuity for Causal Effect Estimation in Epidemiology.
Regression Discontinuity for Causal Effect Estimation in Epidemiology.
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DOI：
10.1007/s40471-016-0080-x
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发表时间：
2016
影响因子：
3.3
通讯作者：
Bärnighausen T
中科院分区：
文献类型：
--
作者：
Oldenburg CE;Moscoe E;Bärnighausen T
当一个连续测量的变量被用于基于阈值规则将暴露分配给个体时，回归不连续性分析可以生成暴露的因果效应的估计。预期略高于阈值的个体在测量和未测量基线协变量的分布方面与略低于阈值的个体相似，从而导致互换。在阈值处，如果在连续分配变量中存在随机变化，则保证交换，例如，由于随机测量误差。在交换条件下，因果效应可以在阈值处确定。回归不连续性意向治疗（RD-ITT）对结果的影响可以估计为刚好高于（或低于）与刚好低于（或高于）阈值的个体之间的结果差异。该效应类似于随机对照试验中的ITT效应。工具变量法可以用来估计暴露本身的影响，利用阈值作为工具。我们回顾了最近的流行病学文献报告回归不连续性研究，发现虽然回归不连续性设计开始在流行病学的各种应用中使用，但它们仍然相对罕见，分析和报告实践各不相同。回归不连续性有可能大大有助于流行病学的证据基础，特别是关于根据阈值规则提供的医疗的实际和长期影响和副作用-例如低出生体重、高血压或糖尿病的治疗。
Regression discontinuity analyses can generate estimates of the causal effects of an exposure when a continuously measured variable is used to assign the exposure to individuals based on a threshold rule. Individuals just above the threshold are expected to be similar in their distribution of measured and unmeasured baseline covariates to individuals just below the threshold, resulting in exchangeability. At the threshold exchangeability is guaranteed if there is random variation in the continuous assignment variable, e.g., due to random measurement error. Under exchangeability, causal effects can be identified at the threshold. The regression discontinuity intention-to-treat (RD-ITT) effect on an outcome can be estimated as the difference in the outcome between individuals just above (or below) versus just below (or above) the threshold. This effect is analogous to the ITT effect in a randomized controlled trial. Instrumental variable methods can be used to estimate the effect of exposure itself utilizing the threshold as the instrument. We review the recent epidemiologic literature reporting regression discontinuity studies and find that while regression discontinuity designs are beginning to be utilized in a variety of applications in epidemiology, they are still relatively rare, and analytic and reporting practices vary. Regression discontinuity has the potential to greatly contribute to the evidence base in epidemiology, in particular on the real-life and long-term effects and side-effects of medical treatments that are provided based on threshold rules – such as treatments for low birth weight, hypertension or diabetes.