Mapping onto Eq-5 D for patients in poor health.
Mapping onto Eq-5 D for patients in poor health.
复制标题
DOI：
10.1186/1477-7525-8-141
复制
发表时间：
2010-11-26
影响因子：
3.6
通讯作者：
Stolk EA
中科院分区：
文献类型：
--
作者：
Versteegh MM;Rowen D;Brazier JE;Stolk EA
越来越多的研究报告了使用疾病特异性非偏好测量预测EQ-5 D效用值的映射算法。然而，已经发现许多映射算法系统性地过度预测健康状况不佳患者的EQ-5 D效用值。目前没有关于如何处理这一问题的指导方针。本文关注的问题是为什么在健康状况不佳的患者中会出现EQ-5 D效用值的高估，并探讨可能的解决方案。使用三个现有数据集估计映射算法，并评估文献中将癌症特异性EORTC-QLQ C-30和关节炎特异性健康评估问卷（HAQ）映射到EQ-5 D的现有映射算法。单独的映射算法估计不良的健康状况。使用QLQ-C30和HAQ的截止点定义不良健康状态，该截止点使用与EQ-5 D值的关联来确定。所有映射算法都存在对健康状况不佳患者的效用值过度预测的问题。EQ-5 D量表中报告的“极端问题”大幅减少，任何EQ-5 D维度中最严重水平的观察结果很少，任何EQ-5 D维度中最不严重水平的观察结果很多，导致EQ-5 D指数值呈双峰分布，这与健康状况不佳患者的效用值预测过高有关。本文提出了单独的算法来预测健康状况不佳患者的效用值，其中这些是使用HAQ-DI（> 2.0）和QLQ C-30（QLQ C-30功能量表的平均值< 45）的截止点选择的。QLQ-C30单独算法在预测健康状况不佳患者的效用值方面优于现有的映射算法，但仍然不能准确预测平均效用值。由于数据限制，无法估计HAQ单独算法。映射算法过度预测了健康状况不佳患者的效用值，但仍用于成本效益分析。可以制定关于何时不适当使用绘图算法的准则，例如通过确定截止点。疾病特异性问卷上的截止点可以通过与过度预测的原因相关联来确定。本研究中发现的截止点代表健康严重受损。使用单独的映射算法来预测健康状况不佳的个人的效用值，大大减少了过度预测，但并不能完全解决问题。
An increasing amount of studies report mapping algorithms which predict EQ-5 D utility values using disease specific non-preference-based measures. Yet many mapping algorithms have been found to systematically overpredict EQ-5 D utility values for patients in poor health. Currently there are no guidelines on how to deal with this problem. This paper is concerned with the question of why overestimation of EQ-5 D utility values occurs for patients in poor health, and explores possible solutions. Three existing datasets are used to estimate mapping algorithms and assess existing mapping algorithms from the literature mapping the cancer-specific EORTC-QLQ C-30 and the arthritis-specific Health Assessment Questionnaire (HAQ) onto the EQ-5 D. Separate mapping algorithms are estimated for poor health states. Poor health states are defined using a cut-off point for QLQ-C30 and HAQ, which is determined using association with EQ-5 D values. All mapping algorithms suffer from overprediction of utility values for patients in poor health. The large decrement of reporting 'extreme problems' in the EQ-5 D tariff, few observations with the most severe level in any EQ-5 D dimension and many observations at the least severe level in any EQ-5 D dimension led to a bimodal distribution of EQ-5 D index values, which is related to the overprediction of utility values for patients in poor health. Separate algorithms are here proposed to predict utility values for patients in poor health, where these are selected using cut-off points for HAQ-DI (> 2.0) and QLQ C-30 (< 45 average of QLQ C-30 functioning scales). The QLQ-C30 separate algorithm performed better than existing mapping algorithms for predicting utility values for patients in poor health, but still did not accurately predict mean utility values. A HAQ separate algorithm could not be estimated due to data restrictions. Mapping algorithms overpredict utility values for patients in poor health but are used in cost-effectiveness analyses nonetheless. Guidelines can be developed on when the use of a mapping algorithms is inappropriate, for instance through the identification of cut-off points. Cut-off points on a disease specific questionnaire can be identified through association with the causes of overprediction. The cut-off points found in this study represent severely impaired health. Specifying a separate mapping algorithm to predict utility values for individuals in poor health greatly reduces overprediction, but does not fully solve the problem.
登录
查看更多内容
影响因子：
45.3
作者：
Doorduijn, JK;van der Holt, B;Sonneveld, P
通讯作者：
Sonneveld, P
影响因子：
4.5
作者：
McKenzie, Lynda;van der Pol, Marjon
通讯作者：
van der Pol, Marjon
影响因子：
4.5
作者：
Ara, Roberta;Brazier, John
通讯作者：
Brazier, John
影响因子：
2.3
作者：
Yang, Min;Dubois, Dominique;Gajria, Kavita
通讯作者：
Gajria, Kavita
DOI：
10.1093/jnci/85.5.365
发表时间：
1993-03-03
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
AARONSON, NK;AHMEDZAI, S;TAKEDA, F
通讯作者：
TAKEDA, F