Li Liang, Wu Chih-Hsien, Ning Jing, Huang Xuelin, Tina Shih Ya-Chen, Shen Yu
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, 77030.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center.
J Am Stat Assoc. 2018;113(522):582-592. doi: 10.1080/01621459.2017.1361329. Epub 2018 Jun 18.
Estimating the average monthly medical costs from disease diagnosis to a terminal event such as death for an incident cohort of patients is a topic of immense interest to researchers in health policy and health economics because patterns of average monthly costs over time reveal how medical costs vary across phases of care. The statistical challenges to estimating monthly medical costs longitudinally are multifold; the longitudinal cost trajectory (formed by plotting the average monthly costs from diagnosis to the terminal event) is likely to be nonlinear, with its shape depending on the time of the terminal event, which can be subject to right censoring. The goal of this paper is to tackle this statistically challenging topic by estimating the conditional mean cost at any month given the time of the terminal event . The longitudinal cost trajectories with different terminal event times form a bivariate surface of and , under the constraint ≤ . We propose to estimate this surface using bivariate penalized splines in an Expectation-Maximization algorithm that treats the censored terminal event times as missing data. We evaluate the proposed model and estimation method in simulations and apply the method to the medical cost data of an incident cohort of stage IV breast cancer patients from the Surveillance, Epidemiology and End Results-Medicare Linked Database.
估算某一发病队列患者从疾病诊断到死亡等终末事件期间的平均每月医疗费用，是卫生政策和卫生经济学领域研究人员极为感兴趣的一个话题，因为平均每月费用随时间的变化模式揭示了医疗费用在不同护理阶段是如何变化的。纵向估算每月医疗费用面临诸多统计挑战；纵向费用轨迹（通过绘制从诊断到终末事件的平均每月费用形成）可能是非线性的，其形状取决于终末事件的时间，而终末事件可能会受到右删失的影响。本文的目标是通过在已知终末事件时间的情况下估算任意月份的条件平均费用，来解决这个具有统计挑战性的话题。在 ≤ 的约束下，具有不同终末事件时间的纵向费用轨迹形成一个关于 和 的二元曲面。我们建议在期望最大化算法中使用二元惩罚样条来估算这个曲面，该算法将删失的终末事件时间视为缺失数据。我们在模拟中评估所提出的模型和估算方法，并将该方法应用于来自监测、流行病学和最终结果 - 医疗保险链接数据库的IV期乳腺癌患者发病队列的医疗费用数据。
