Kharroubi Samer, Brazier John E, O'Hagan Anthony
University of York, UK.
Soc Sci Med. 2007 Mar;64(6):1242-52. doi: 10.1016/j.socscimed.2006.10.040. Epub 2006 Dec 8.
It has long been recognised that respondent characteristics can impact on the values they give to health states. This paper reports on the findings from applying a non-parametric approach to estimate the covariates in a model of SF-6D health state values using Bayesian methods. The data set is the UK SF-6D valuation study, where a sample of 249 states defined by the SF-6D (a derivate of the SF-36) was valued by a sample of the UK general population using standard gamble. Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics and that it allows for an impact to vary by health state (whilst ensuring that full health passes through unity). The results suggest an important age effect, with sex, class, education, employment and physical functioning probably having some effect, but the remaining covariates having no discernable effect. Adjusting for covariates in the UK sample made little difference to mean health state values. The paper discusses the implications of these results for policy.
长期以来，人们已经认识到应答者的特征会影响他们赋予健康状态的价值。本文报告了使用贝叶斯方法应用非参数方法估计SF - 6D健康状态值模型中的协变量的研究结果。数据集是英国SF - 6D估值研究，其中由SF - 6D（SF - 36的衍生物）定义的249种状态的样本由英国普通人群样本使用标准博弈法进行估值。非参数模型的优点是它可用于预测具有不同特征分布的人群中的分数，并且它允许影响因健康状态而异（同时确保完全健康通过统一标准）。结果表明年龄有重要影响，性别、阶层、教育、就业和身体功能可能有一些影响，但其余协变量没有明显影响。在英国样本中调整协变量对平均健康状态值影响不大。本文讨论了这些结果对政策的影响。
