A flexible approach for causal inference with multiple treatments and clustered survival outcomes.
A flexible approach for causal inference with multiple treatments and clustered survival outcomes.
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一种灵活的因果推断方法，具有多种治疗方法和集群生存结果。
DOI：
10.1002/sim.9548
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发表时间：
2022-11-10
影响因子：
2
通讯作者：
Hogan, Joseph W.
中科院分区：
文献类型：
--
作者：
Hu, Liangyuan;Ji, Jiayi;Ennis, Ronald D.;Hogan, Joseph W.
当使用观察数据对多个治疗对集群生存结果的影响进行因果推断时，我们需要解决多水平数据结构、多个治疗、审查和因果分析的未测量混杂的含义。几乎没有现成的因果推理工具可以同时解决这些问题。我们建立了一个灵活的随机截取加速失效时间模型，其中我们使用贝叶斯加性回归树来捕捉截尾生存时间和处理前协变量之间的任意复杂关系，并使用随机截获来捕捉特定于簇的主效应。我们开发了一种有效的马尔可夫链蒙特卡罗算法来得出关于多个处理的种群生存效应的后验推断，并检验聚类级效应的变异性。我们进一步提出了一种可解释的敏感性分析方法来评估所得出的关于治疗效果的因果推断对偏离没有不可测量的混杂的因果假设的潜在程度的敏感性。扩展的模拟实验验证和展示了我们所提出的方法良好的实际操作特性。将所提出的方法应用于来自国家癌症数据库的老年高危局部性前列腺癌患者的数据集，我们评估了三种治疗方法对患者生存的比较效果，并评估了潜在的不可测量的混杂的后果。在这项工作中开发的方法在R包riAFTBART中很容易获得。
When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of multiple treatments and examine the variability in cluster-level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high-risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding. The methods developed in this work are readily available in the R package riAFTBART.
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