A doubly robust approach for cost-effectiveness estimation from observational data
A doubly robust approach for cost-effectiveness estimation from observational data
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DOI：
10.1177/0962280217693262
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发表时间：
2018-10-01
影响因子：
2.3
通讯作者：
Mitra, Nandita
中科院分区：
文献类型：
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作者：
Li, Jiaqi;Vachani, Anil;Mitra, Nandita
由于需要考虑到信息审查和数据固有的偏斜性，对包括增量成本效益比和净货币效益在内的共同成本效益计量的估计变得复杂。此外，由于这些措施的两个组成部分，医疗费用和生存往往是从观察索赔数据，必须考虑潜在的混杂因素。我们提出了一种新的双重鲁棒性，无偏估计的成本效益的基础上的倾向分数，允许纳入成本的历史和随时间变化的协变量。此外，我们使用集成机器学习方法从参数和非参数成本和倾向得分模型中获得改进的预测。我们的模拟研究表明，所提出的双重鲁棒性的方法，即使在错误指定的倾向得分模型或结果模型。我们使用SEER医疗保险数据，将我们的方法应用于两种竞争性肺癌监测程序（CT与胸部X光）的成本效益分析。
Estimation of common cost-effectiveness measures, including the incremental cost-effectiveness ratio and the net monetary benefit, is complicated by the need to account for informative censoring and inherent skewness of the data. In addition, since the two components of these measures, medical costs and survival are often collected from observational claims data, one must account for potential confounders. We propose a novel doubly robust, unbiased estimator for cost-effectiveness based on propensity scores that allow the incorporation of cost history and time-varying covariates. Further, we use an ensemble machine learning approach to obtain improved predictions from parametric and non-parametric cost and propensity score models. Our simulation studies demonstrate that the proposed doubly robust approach performs well even under mis-specification of either the propensity score model or the outcome model. We apply our approach to a cost-effectiveness analysis of two competing lung cancer surveillance procedures, CT vs. chest X-ray, using SEER-Medicare data.