Predicting Utility for Joint Health States: A General Framework and a New Nonparametric Estimator
Predicting Utility for Joint Health States: A General Framework and a New Nonparametric Estimator
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DOI：
10.1177/0272989x10374508
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发表时间：
2010-09-01
影响因子：
3.6
通讯作者：
Fu, Alex Z.
中科院分区：
文献类型：
--
作者：
Hu, Bo;Fu, Alex Z.
效用测量在临床决策和成本效益分析中很重要，因为公用事业经常被用来计算经质量调整的预期寿命，这是一种用于衡量卫生保健计划和医疗干预措施有效性的指标。由于人口老龄化和合并症的日益普遍，联合健康状态的预测效用已成为一个越来越有价值的研究课题。尽管乘法估计、最小估计和加法估计在实践中经常使用，但研究表明它们都是有偏差的。在这项研究中，作者提出了一个预测关节健康状态效用的一般框架。该框架将这3个非参数估计量作为特例包括在内。在该框架下，引入了一种新的简单的非参数估计--调整减量估计[U-ij=U-min-U-min(1-U-i)(1-U-j)]。当应用于两个独立的数据源时，新的非参数估计不仅产生了对联合健康状态的效用的无偏预测，而且与其他非参数和参数估计相比，具有最小的均方根误差和最高的一致性。这种新的估计器还需要进一步的研究和验证。
Measuring utility is important in clinical decision making and cost-effectiveness analysis because utilities are often used to compute quality-adjusted life expectancy, a metric used in measuring the effectiveness of health care programs and medical interventions. Predicting utility for joint health states has become an increasingly valuable research topic because of the aging of the population and the increasing prevalence of comorbidities. Although multiplicative, minimum, and additive estimators are commonly used in practice, research has shown that they are all biased. In this study, the authors propose a general framework for predicting utility for joint health states. This framework includes these 3 nonparametric estimators as special cases. A new simple nonparametric estimator, the adjusted decrement estimator, [U-ij = U-min - U-min(1 - U-i)(1 - U-j)], is introduced under the proposed framework. When applied to 2 independent data sources, the new nonparametric estimator not only generated unbiased prediction of utilities for joint health states but also had the least root mean squared error and highest concordance when compared with other nonparametric and parametric estimators. Further research and validation of this new estimator are needed.