Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences, American University of Beirut, P.O.BOX: 11-0236, Riad El Solh, Beirut 1107-2020, Lebanon.
Nutrition and Health Sciences, Laney Graduate School, Emory University, Atlanta, GA 30322, USA.
Int J Environ Res Public Health. 2020 Jun 2;17(11):3949. doi: 10.3390/ijerph17113949.
The parameter uncertainty in the six-dimensional health state short form (SF-6D) value sets is commonly ignored. There are two sources of parameter uncertainty: uncertainty around the estimated regression coefficients and uncertainty around the model's specification. This study explores these two sources of parameter uncertainty in the value sets using probabilistic sensitivity analysis (PSA) and a Bayesian approach.
We used data from the original UK/SF-6D valuation study to evaluate the extent of parameter uncertainty in the value set. First, we re-estimated the Brazier model to replicate the published estimated coefficients. Second, we estimated standard errors around the predicted utility of each SF-6D state to assess the impact of parameter uncertainty on these estimated utilities. Third, we used Monte Carlo simulation technique to account for the uncertainty on these estimates. Finally, we used a Bayesian approach to quantifying parameter uncertainty in the value sets. The extent of parameter uncertainty in SF-6D value sets was assessed using data from the Hong Kong valuation study.
Including parameter uncertainty results in wider confidence/credible intervals and improved coverage probability using both approaches. Using PSA, the mean 95% confidence intervals widths for the mean utilities were 0.1394 (range: 0.0565-0.2239) and 0.0989 (0.0048-0.1252) with and without parameter uncertainty whilst, using the Bayesian approach, this was 0.1478 (0.053-0.1665). Upon evaluating the impact of parameter uncertainty on estimates of a population's mean utility, the true standard error was underestimated by 79.1% (PSA) and 86.15% (Bayesian) when parameter uncertainty was ignored.
Parameter uncertainty around the SF-6D value set has a large impact on the predicted utilities and estimated confidence intervals. This uncertainty should be accounted for when using SF-6D utilities in economic evaluations. Ignoring this additional information could impact misleadingly on policy decisions.
六维度健康状态简式量表（SF-6D）值集中的参数不确定性通常被忽略。参数不确定性有两个来源：估计回归系数的不确定性和模型规范的不确定性。本研究使用概率敏感性分析（PSA）和贝叶斯方法探讨了值集中这两个参数不确定性来源。
我们使用来自英国/SF-6D 估值研究的原始数据来评估值集中参数不确定性的程度。首先，我们重新估计了 Brazier 模型以复制已发表的估计系数。其次，我们估计了每个 SF-6D 状态的预测效用的标准误差，以评估参数不确定性对这些估计效用的影响。第三，我们使用蒙特卡罗模拟技术来考虑这些估计的不确定性。最后，我们使用贝叶斯方法来量化值集中的参数不确定性。使用来自香港估值研究的数据评估了 SF-6D 值集中参数不确定性的程度。
使用两种方法，包含参数不确定性都会导致置信/可信区间更宽，覆盖概率更高。使用 PSA，在有和没有参数不确定性的情况下，平均效用的 95%置信区间宽度分别为 0.1394（范围：0.0565-0.2239）和 0.0989（0.0048-0.1252），而使用贝叶斯方法，这分别为 0.1478（0.053-0.1665）。在评估参数不确定性对人群平均效用估计的影响时，当忽略参数不确定性时，真实标准误差被低估了 79.1%（PSA）和 86.15%（贝叶斯）。
SF-6D 值集中参数不确定性对预测效用和估计置信区间有很大影响。在经济评估中使用 SF-6D 效用时，应考虑这种不确定性。忽略此附加信息可能会对政策决策产生误导性影响。
