Statistical modeling of longitudinal medical cost trajectory: renal cell cancer care cost analyses.
Statistical modeling of longitudinal medical cost trajectory: renal cell cancer care cost analyses.
复制标题
纵向医疗成本轨迹的统计模型：肾细胞癌护理成本分析。
DOI：
10.1093/biostatistics/kxab024
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Li,Liang
中科院分区：
文献类型：
--
作者：
Wang,Shikun;Shen,Yu;Shih,Ya-ChenTina;Xu,Ying;Li,Liang
估计当前癌症护理的成本对卫生政策制定者来说很重要。成本预测中不可或缺的一步是使用纵向医疗成本数据估计癌症患者事件队列的成本轨迹，考虑到死亡等晚期事件，以及由于失去随访而进行的正确审查。由于癌症护理成本和存活率是相关的，在这方面，一个具有科学意义的推论数量是以存活率为条件的平均成本轨迹。我们提出了一种两阶段半参数方法，从纵向医疗成本和生存的联合模型中估计纵向成本轨迹。不同生存时间对应的纵向费用轨迹在一个三角形区域内形成一个二元曲面。使用离散化测量时间和生存时间的张量积以及二维数组中数据的有效脊罚罚来估计成本轨迹。所提出的方法平衡了模型灵活性、统计效率和计算可处理性的实际考虑。我们使用所提出的方法，使用监测、流行病学和最终结果-医疗保险关联数据库来估计肾细胞癌患者的成本轨迹。
Estimating the current cost of cancer care is important to health policy makers. An indispensable step in cost projection is to estimate cost trajectories from an incident cohort of cancer patients using longitudinal medical cost data, accounting for terminal events such as death, and right censoring due to loss of follow-up. Since the cost of cancer care and survival are correlated, a scientifically meaningful quantity for inference in this context is the mean cost trajectory conditional on survival. We propose a two-stage semiparametric approach to estimate the longitudinal cost trajectories from a joint model of longitudinal medical costs and survival. The longitudinal cost trajectories corresponding to various survival times form a bivariate surface in a triangular area. The cost trajectories are estimated using the tensor products of discretized measurement time and survival, as well as effective ridge penalties for data in 2D arrays. The proposed approach balances the practical considerations of model flexibility, statistical efficiency, and computational tractability. We used the proposed method to estimate the cost trajectories of renal cell cancer patients using the Surveillance, Epidemiology, and End Results-Medicare linked database.
DOI：
10.1214/09-aoas319
发表时间：
2010-09-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Gary Chan KC;Wang MC
通讯作者：
Wang MC