Estimating the Effect of a Time-Dependent Treatment by Levels of an Internal Time-Dependent Covariate: Application to the Contrast Between Liver Wait-List and Posttransplant Mortality
Estimating the Effect of a Time-Dependent Treatment by Levels of an Internal Time-Dependent Covariate: Application to the Contrast Between Liver Wait-List and Posttransplant Mortality
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DOI：
10.1198/jasa.2009.0003
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发表时间：
2009-03-01
影响因子：
3.7
通讯作者：
Merion, Robert M.
中科院分区：
文献类型：
--
作者：
Schaubel, Douglas E.;Wolfe, Robert A.;Merion, Robert M.
在美国，很少有健康政策问题像器官移植的分配那样受到严格审查，目前正在努力研究现有器官分配算法的有效性。有效的器官定位策略的设计强烈依赖于对移植的存活益处的准确估计。许多器官分配政策根据时间依赖性健康状况指标（内部时间依赖性协变量）对等待名单上的患者进行排序，以便优先考虑等待名单死亡风险最大的候选人。这是非常感兴趣的，以衡量移植的好处，这种健康状况的措施，以确定的状态水平，代表无效或不必要的移植的水平。在存在观察数据的情况下，移植的存活益处迄今为止已经通过对应于二元时间依赖性移植指标变量的参数来量化。使用现有方法的标准时间相关分析的参数（即，用于每个状态水平的单独的移植指示符）难以解释，因为它们在患者处于特定水平时应用;即，它们说明了病人目前的状况，但没有说明病人的状况可能恶化。我们提出了一种新的方法来估计一个时间依赖性治疗的影响，在内部的时间依赖性协变量的水平。该方法产生参数估计，而不是应用于患者的当前健康状况，平均在未来的潜在变化的健康状况。所提出的方法适用于终末期肝病数据从国家器官衰竭登记。
Few health policy issues in the U.S. are scrutinized as intensely as the distribution of organs for transplantation, with much effort currently underway to examine the efficacy of existing organ allocation algorithms. The design of efficient organ a I location policies depends strongly upon accurate estimates of the Survival benefit of transplantation. Many organ allocation policies order patients on the wait-list based on time-dependent health status measures (internal time-dependent covariates), such that priority is given to candidates at the greatest risk for wait-list mortality. It is of great interest to measure the transplant benefit by levels of such health status measures to identify the status levels that represent either futile or unnecessary transplants. In the presence of observational data, the survival benefit of transplantation has, to date, been quantified through the parameter corresponding to a binary time-dependent transplant indicator variable. Parameters from a standard time-dependent analysis using existing methods (i.e., separate transplant indicator for each status level) are difficult to interpret because they apply while the patient is at a particular level; i.e., they account for the patient's current condition, but do not account for the possibility that the patient's condition may worsen. We propose a novel method for estimating the effect of a time-dependent treatment by levels of in internal time-dependent covariate. The method yields parameter estimates which, rather than applying to the patient's current health status, average over future potential changes in health status. The proposed method is applied to end-stage liver disease data obtained from a national organ failure registry.