Odden Michelle C, Zhang Adina, Jawadekar Neal, Tan Annabel, Moran Andrew E, Glymour M Maria, Brayne Carol, Zeki Al Hazzouri Adina, Calonico Sebastian
Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.
Department of Epidemiology and Population Health, Stanford University School of Medicine, 1701 Page Mill Rd., Palo Alto, CA, 94304, USA.
Eur J Epidemiol. 2023 Apr;38(4):393-402. doi: 10.1007/s10654-023-00982-w. Epub 2023 Mar 20.
Regression discontinuity design (RDD) is a quasi-experimental method intended for causal inference in observational settings. While RDD is gaining popularity in clinical studies, there are limited real-world studies examining the performance on estimating known trial casual effects. The goal of this paper is to estimate the effect of statins on myocardial infarction (MI) using RDD and compare with propensity score matching and Cox regression. For the RDD, we leveraged a 2008 UK guideline that recommends statins if a patient's 10-year cardiovascular disease (CVD) risk score > 20%. We used UK electronic health record data from the Health Improvement Network on 49,242 patients aged 65 + in 2008-2011 (baseline) without a history of CVD and no statin use in the two years prior to the CVD risk score assessment. Both the regression discontinuity (n = 19,432) and the propensity score matched populations (n = 24,814) demonstrated good balance of confounders. Using RDD, the adjusted point estimate for statins on MI was in the protective direction and similar to the statin effect observed in clinical trials, although the confidence interval included the null (HR = 0.8, 95% CI 0.4, 1.4). Conversely, the adjusted estimates using propensity score matching and Cox regression remained in the harmful direction: HR = 2.42 (95% CI 1.96, 2.99) and 2.51 (2.12, 2.97). RDD appeared superior to other methods in replicating the known protective effect of statins with MI, although precision was poor. Our findings suggest that, when used appropriately, RDD can expand the scope of clinical investigations aimed at causal inference by leveraging treatment rules from everyday clinical practice.
回归断点设计（RDD）是一种用于观察性研究中因果推断的准实验方法。虽然RDD在临床研究中越来越受欢迎，但检验其在估计已知试验因果效应方面性能的实际研究却很有限。本文的目的是使用RDD估计他汀类药物对心肌梗死（MI）的影响，并与倾向得分匹配和Cox回归进行比较。对于RDD，我们利用了2008年英国的一项指南，该指南建议如果患者的10年心血管疾病（CVD）风险评分>20%，则使用他汀类药物。我们使用了来自健康改善网络的英国电子健康记录数据，这些数据涉及2008 - 2011年（基线）年龄在65岁及以上的49242名患者，他们没有CVD病史，并且在CVD风险评分评估前两年未使用他汀类药物。回归断点组（n = 19432）和倾向得分匹配组（n = 24814）的混杂因素均表现出良好的平衡性。使用RDD，他汀类药物对MI的调整点估计呈保护方向，与临床试验中观察到的他汀类药物效应相似，尽管置信区间包含无效值（HR = 0.8，95%CI 0.4，1.4）。相反，使用倾向得分匹配和Cox回归的调整估计仍呈有害方向：HR = 2.42（95%CI 1.96，2.99）和2.51（2.12，2.97）。在复制他汀类药物对MI的已知保护作用方面，RDD似乎优于其他方法，尽管精度较差。我们的研究结果表明，在适当使用时，RDD可以通过利用日常临床实践中的治疗规则来扩大旨在进行因果推断的临床研究范围。
