Directed Acyclic Graph Assisted Method For Estimating Average Treatment Effect.
Directed Acyclic Graph Assisted Method For Estimating Average Treatment Effect.
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用于估计平均治疗效果的有向无环图辅助方法。
DOI：
10.1080/10543406.2023.2296047
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发表时间：
2023
影响因子：
1.1
通讯作者：
Kong,Maiying
中科院分区：
文献类型：
--
作者：
Sun,Jingchao;Duncan,Scott;Pal,Subhadip;Kong,Maiying
观察数据，如电子临床记录和索赔数据，可以证明是非常宝贵的，以评估平均治疗效果（ATE）和支持决策，只要他们被正确使用。基于倾向评分的治疗权重逆概率（IPTW）方法在估计ATE方面表现出显著的有效性，假设满足交换性、一致性和阳性的假设。有向无环图（DAG）提供了一种实用的方法来评估交换假设，该假设认为治疗分配和潜在结果是独立的，给定一组混淆变量，这些变量阻止了从治疗分配到潜在结果的所有后门路径。为了确保一致的ATE估计量，可以调整干扰变量的最小充分调整集，这些干扰变量阻止从治疗分配到结果的所有后门路径。为了提高ATE估计的效率，我们的建议包括将最低限度充分调整集的混杂变量和预测因子的倾向得分模型。进行了广泛的模拟，以评估性能的倾向评分为基础的IPTW方法估计ATE时，不同的协变量集包括在倾向评分模型。模拟结果强调了在倾向评分模型中包括混杂变量的最小充分调整集沿着预测因子以获得一致且有效的ATE估计量的重要性。我们利用2016年医疗保健成本和利用项目儿童住院患者数据库，应用所提出的方法调查气管切开术是否与住院婴儿死亡率存在因果关系。发现估计的ATE约为2.30%-2.46%，p值>0.05。
Observational data, such as electronic clinical records and claims data, can prove invaluable for evaluating the Average Treatment Effect (ATE) and supporting decision-making, provided they are employed correctly. The Inverse Probability of Treatment Weighting (IPTW) method, based on propensity scores, has demonstrated remarkable efficacy in estimating ATE, assuming that the assumptions of exchangeability, consistency, and positivity are met. Directed Acyclic Graphs (DAGs) offer a practical approach to assess the exchangeability assumption, which asserts that treatment assignment and potential outcomes are independent given a set of confounding variables that block all backdoor paths from treatment assignment to potential outcomes. To ensure a consistent ATE estimator, one can adjust for a minimally sufficient adjustment set of confounding variables that block all backdoor paths from treatment assignment to the outcome. To enhance the efficiency of ATE estimators, our proposal involves incorporating both the minimally sufficient adjustment set of confounding variables and predictors into the propensity score model. Extensive simulations were conducted to evaluate the performance of propensity score-based IPTW methods in estimating ATE when different sets of covariates were included in the propensity score models. The simulation results underscored the significance of including the minimally sufficient adjustment set of confounding variables along with predictors in the propensity score models to obtain a consistent and efficient ATE estimator. We applied this proposed method to investigate whether tracheostomy was causally associated with in-hospital infant mortality, utilizing the 2016 Healthcare Cost and Utilization Project Kids’ Inpatient Database. The estimated ATE was found to be approximately 2.30%–2.46% with p-value >0.05.