Discussion of "On Bayesian estimation of marginal structural models".
Discussion of "On Bayesian estimation of marginal structural models".
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DOI：
10.1111/biom.12273
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发表时间：
2015-06
期刊：
影响因子：
1.9
通讯作者：
Wasserman L
中科院分区：
文献类型：
--
作者：
Robins JM;Hernán MA;Wasserman L
在估计边际治疗效应时，治疗逆概率（IPT）加权的目的是构建一个测量协变量不失衡的伪人群，从而在进行推断时消除混杂和信息删失的影响。在这篇文章中，我们形式化的概念，这样一个伪人口的数据生成机制，具有特定的特点，并表明，这导致了一个自然的贝叶斯解释IPT加权估计。使用这种解释，我们能够提出第一个完全贝叶斯过程估计参数的边际结构模型使用IPT加权。我们的方法表明，权重应来自后验预测治疗分配和删失概率，回答的问题，是否以及如何在估计的权重的不确定性应纳入贝叶斯推断的边际治疗效果。所提出的方法相比，现有的方法在模拟数据，并应用于加拿大合并感染队列的分析。
The purpose of inverse probability of treatment (IPT) weighting in estimation of marginal treatment effects is to construct a pseudo-population without imbalances in measured covariates, thus removing the effects of confounding and informative censoring when performing inference. In this article, we formalize the notion of such a pseudo-population as a data generating mechanism with particular characteristics, and show that this leads to a natural Bayesian interpretation of IPT weighted estimation. Using this interpretation, we are able to propose the first fully Bayesian procedure for estimating parameters of marginal structural models using an IPT weighting. Our approach suggests that the weights should be derived from the posterior predictive treatment assignment and censoring probabilities, answering the question of whether and how the uncertainty in the estimation of the weights should be incorporated in Bayesian inference of marginal treatment effects. The proposed approach is compared to existing methods in simulated data, and applied to an analysis of the Canadian Co-infection Cohort.