Uncertainty in the design stage of two-stage Bayesian propensity score analysis.
Uncertainty in the design stage of two-stage Bayesian propensity score analysis.
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DOI：
10.1002/sim.8486
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发表时间：
2020-07-30
影响因子：
2
通讯作者：
Zigler CM
中科院分区：
文献类型：
--
作者：
Liao SX;Zigler CM
倾向评分分析（PSA）的两阶段过程包括设计阶段和分析阶段，在设计阶段，估计并实施倾向评分（PS）以近似随机化实验，在分析阶段，根据设计条件估计治疗效果。本文考虑如何与设计阶段的不确定性影响因果效应的估计在分析阶段。这种设计不确定性可能来自于PS本身是一个估计量的事实，也可能来自于与PS实现选择相关的设计阶段的其他特征。本文提供了一个程序，获得后验分布的因果效应边缘化后的分布设计阶段的输出，贷款的形式化程度的贝叶斯方法PSA，在最近的文献中获得了关注。设计阶段输出的概率分布的制定取决于PS在设计阶段的实施方式，不确定性传播到因果估计取决于在分析阶段如何估计治疗效果。我们探讨了这些差异内的一个样本中常用的PS实现（分位数分层，最近邻匹配，卡尺匹配，治疗加权的逆概率，双稳健估计），并调查在模拟研究中的统计学家的选择PS模型和实现的程度之间和设计内的变异性估计的治疗效果的影响。然后，这些方法被部署在细颗粒物空气污染水平与燃煤电厂排放量升高之间的关联的调查中。
The two-stage process of propensity score analysis (PSA) includes a design stage where propensity scores (PSs) are estimated and implemented to approximate a randomized experiment and an analysis stage where treatment effects are estimated conditional on the design. This article considers how uncertainty associated with the design stage impacts estimation of causal effects in the analysis stage. Such design uncertainty can derive from the fact that the PS itself is an estimated quantity, but also from other features of the design stage tied to choice of PS implementation. This article offers a procedure for obtaining the posterior distribution of causal effects after marginalizing over a distribution of design-stage outputs, lending a degree of formality to Bayesian methods for PSA that have gained attention in recent literature. Formulation of a probability distribution for the design-stage output depends on how the PS is implemented in the design stage, and propagation of uncertainty into causal estimates depends on how the treatment effect is estimated in the analysis stage. We explore these differences within a sample of commonly used PS implementations (quantile stratification, nearest-neighbor matching, caliper matching, inverse probability of treatment weighting, and doubly robust estimation) and investigate in a simulation study the impact of statistician choice in PS model and implementation on the degree of between- and within-design variability in the estimated treatment effect. The methods are then deployed in an investigation of the association between levels of fine particulate air pollution and elevated exposure to emissions from coal-fired power plants.
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DOI：
10.1007/s10928-008-9109-1
发表时间：
2009-02-01
影响因子：
2.5
作者：
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通讯作者：
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发表时间：
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DOI：
10.1111/j.1467-9531.2010.01226.x
发表时间：
2010-01-01
期刊：
SOCIOLOGICAL METHODOLOGY, VOL 40
影响因子：
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通讯作者：
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影响因子：
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