A Bayesian nonparametric model for zero-inflated outcomes: Prediction, clustering, and causal estimation.
A Bayesian nonparametric model for zero-inflated outcomes: Prediction, clustering, and causal estimation.
复制标题
零膨胀结果的贝叶斯非参数模型：预测、聚类和因果估计。
DOI：
10.1111/biom.13244
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发表时间：
2021
期刊：
影响因子：
1.9
通讯作者：
Roy,JasonA
中科院分区：
文献类型：
--
作者：
Oganisian,Arman;Mitra,Nandita;Roy,JasonA
研究人员通常对预测结果，检测其数据的不同亚组或估计因果治疗效果感兴趣。表现出偏斜和零膨胀的病理数据分布使这些任务变得复杂，需要高度灵活的数据自适应建模。在本文中，我们提出了一个多用途的贝叶斯非参数模型的连续，零膨胀的结果，同时预测结构零，捕捉偏度，并集群患者相似的联合数据分布。我们的方法的灵活性产生的预测，捕捉联合数据分布比常用的零膨胀的方法。此外，我们证明，我们的模型可以连贯地纳入一个标准化程序，用于计算因果效应估计，这些数据病理是强大的。在这个模型的各个层次的不确定性流到因果效应估计的兴趣，允许简单的点估计，区间估计，验证阳性，所需的因果识别假设和后验预测检查。我们的模拟结果表明，在复杂的数据设置下，点估计有低偏差和区间估计有接近名义覆盖。在更简单的设置下，这些结果保持不变，同时比比较方法产生更低的效率损失。我们使用我们提出的方法来分析零膨胀的住院医疗费用子宫内膜癌患者接受化疗或放射治疗的SEER医疗保险数据库。
Researchers are often interested in predicting outcomes, detecting distinct subgroups of their data, or estimating causal treatment effects. Pathological data distributions that exhibit skewness and zero-inflation complicate these tasks—requiring highly flexible, data-adaptive modeling. In this paper, we present a multipurpose Bayesian nonparametric model for continuous, zero-inflated outcomes that simultaneously predicts structural zeros, captures skewness, and clusters patients with similar joint data distributions. The flexibility of our approach yields predictions that capture the joint data distribution better than commonly used zero-inflated methods. Moreover, we demonstrate that our model can be coherently incorporated into a standardization procedure for computing causal effect estimates that are robust to such data pathologies. Uncertainty at all levels of this model flow through to the causal effect estimates of interest—allowing easy point estimation, interval estimation, and posterior predictive checks verifying positivity, a required causal identification assumption. Our simulation results show point estimates to have low bias and interval estimates to have close to nominal coverage under complicated data settings. Under simpler settings, these results hold while incurring lower efficiency loss than comparator methods. We use our proposed method to analyze zero-inflated inpatient medical costs among endometrial cancer patients receiving either chemotherapy or radiation therapy in the SEER-Medicare database.