论文标题
基于图的多变量协变量平衡测试在多价值处理下
Graph-Based Tests for Multivariate Covariate Balance Under Multi-Valued Treatments
论文作者
论文摘要
我们建议使用非参数，基于图的测试来评估通过多价处理的观察性研究中协变量的分布平衡。我们的测试利用了图形结构，从将所有数据连接到最近的邻居图的汉密尔顿路径等图。我们考虑形成最小距离图的算法，例如最佳的哈密顿路径或非双方匹配，或近似替代方案，例如贪婪的哈密顿路径或贪婪的最近的邻居图。广泛的模拟研究表明，所提出的测试能够检测其他方法错过的匹配模型的错误指定。与直觉相反，我们还发现，在大多数情况下，在良好形式的近似图上进行的测试比在最佳形成的图表上进行的测试更好，并且在大约最近的邻居图上进行了正确形成的测试，平均表现最佳。在具有乳腺癌数据的多值治疗设置中，这些基于图的测试也可以检测出常见匹配诊断遗漏而错过的失衡。我们提供了一个新的r套件GraphTest来实现这些方法并重现我们的结果。
We propose the use of non-parametric, graph-based tests to assess the distributional balance of covariates in observational studies with multi-valued treatments. Our tests utilize graph structures ranging from Hamiltonian paths that connect all of the data to nearest neighbor graphs that maximally separates data into pairs. We consider algorithms that form minimal distance graphs, such as optimal Hamiltonian paths or non-bipartite matching, or approximate alternatives, such as greedy Hamiltonian paths or greedy nearest neighbor graphs. Extensive simulation studies demonstrate that the proposed tests are able to detect the misspecification of matching models that other methods miss. Contrary to intuition, we also find that tests ran on well-formed approximate graphs do better in most cases than tests run on optimally formed graphs, and that a properly formed test on an approximate nearest neighbor graph performs best, on average. In a multi-valued treatment setting with breast cancer data, these graph-based tests can also detect imbalances otherwise missed by common matching diagnostics. We provide a new R package graphTest to implement these methods and reproduce our results.
