Estimation of causal effects of multiple treatments in observational studies with a binary outcome.
Estimation of causal effects of multiple treatments in observational studies with a binary outcome.
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DOI：
10.1177/0962280220921909
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发表时间：
2020-11
影响因子：
2.3
通讯作者：
Wisnivesky J
中科院分区：
文献类型：
--
作者：
Hu L;Gu C;Lopez M;Ji J;Wisnivesky J
当结果是二元时，缺乏可靠的方法来估计多种治疗的因果效应。本文使用两组独特的模拟来提出和评估贝叶斯加性回归树在此类设置中的使用。首先，我们将贝叶斯加性回归树与针对连续结果提出的几种方法进行比较，包括治疗加权的逆概率、目标最大似然估计、向量匹配和回归调整。结果表明，在治疗分配和结果生成机制的非线性和非可加性条件下，贝叶斯加性回归树、目标最大似然估计和使用广义增强模型的治疗加权逆概率提供了更好的偏差减少和更小的均方根误差。贝叶斯加性回归树和目标最大似然估计器提供更一致的 95% 置信区间覆盖和更好的大样本收敛特性。其次，我们为贝叶斯加性回归树提供了一种策略来识别公共支持区域，以保留推理单元并避免在不存在公共支持的协变量空间区域上进行外推。贝叶斯加性回归树比基于广义倾向得分的策略保留更多的推理单元，并且在协变量重叠程度不同的各种场景中，与目标最大似然估计或广义提升模型相比，表现出较低的偏差。一项研究三种非小细胞肺癌手术方法效果的案例研究展示了这些方法。
There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, Bayesian additive regression trees, targeted maximum likelihood estimator, and inverse probability of treatment weighting using generalized boosted models provide better bias reduction and smaller root mean squared error. Bayesian additive regression trees and targeted maximum likelihood estimator provide more consistent 95% confidence interval coverage and better large-sample convergence property. Second, we supply Bayesian additive regression trees with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. Bayesian additive regression trees retain more inferential units than the generalized propensity score-based strategy, and shows lower bias, compared to targeted maximum likelihood estimator or generalized boosted model, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods.
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影响因子：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
DOI：
10.1198/016214504000001187
发表时间：
2004-09-01
影响因子：
3.7
作者：
Imai, K;van Dyk, DA
通讯作者：
van Dyk, DA
影响因子：
2
作者：
Feng, Ping;Zhou, Xiao-Hua;Li, Xiao-Song
通讯作者：
Li, Xiao-Song
影响因子：
5
作者：
Cole, Stephen R.;Hernan, Miguel A.
通讯作者：
Hernan, Miguel A.
影响因子：
3.7
作者：
Lee BK;Lessler J;Stuart EA
通讯作者：
Stuart EA