论文标题
近似贝叶斯引导程序，以估计具有2型糖尿病治疗方案的观察性研究中的多级治疗效应
Approximate Bayesian Bootstrap Procedures to Estimate Multilevel Treatment Effects in Observational Studies with Application to Type 2 Diabetes Treatment Regimens
论文作者
论文摘要
随机临床试验被认为是估计因果影响的金标准。然而，在旨在检查干预措施不利影响的研究中，由于道德和财务考虑，此类试验通常是不切实际的。在观察性研究中，提出了对广义倾向得分的匹配，作为估计多种干预措施的治疗效果的可能解决方案。但是，这些匹配过程的点和间隔估计值的推导可能会随着非连续或审查的结果而变得复杂。我们提出了一种新型的近似贝叶斯自举算法，该算法可导致统计上有效的点和间隔估计，并具有分类结果。该过程依赖于估计的广义倾向得分，并乘以每个单元的未观察到的潜在结果。此外，我们描述了相应的可解释的灵敏度分析，以检查不合子的假设。我们应用这种方法来检查大型观察数据库中2型糖尿病的常见，现实世界抗糖尿病治疗方案的心血管安全。
Randomized clinical trials are considered the gold standard for estimating causal effects. Nevertheless, in studies that are aimed at examining adverse effects of interventions, such trials are often impractical because of ethical and financial considerations. In observational studies, matching on the generalized propensity scores was proposed as a possible solution to estimate the treatment effects of multiple interventions. However, the derivation of point and interval estimates for these matching procedures can become complex with non-continuous or censored outcomes. We propose a novel Approximate Bayesian Bootstrap algorithm that result in statistically valid point and interval estimates of the treatment effects with categorical outcomes. The procedure relies on the estimated generalized propensity scores and multiply imputes the unobserved potential outcomes for each unit. In addition, we describe a corresponding interpretable sensitivity analysis to examine the unconfoundedness assumption. We apply this approach to examines the cardiovascular safety of common, real-world anti-diabetic treatment regimens for Type 2 diabetes mellitus in a large observational database.
