Kharroubi Samer A
Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut 1107-2020, Lebanon.
Healthcare (Basel). 2020 Dec 1;8(4):525. doi: 10.3390/healthcare8040525.
: Typically, modeling of health-related quality of life data is often troublesome since its distribution is positively or negatively skewed, spikes at zero or one, bounded and heteroscedasticity. : In the present paper, we aim to investigate whether Bayesian beta regression is appropriate for analyzing the SF-6D health state utility scores and respondent characteristics. : A sample of 126 Lebanese members from the American University of Beirut valued 49 health states defined by the SF-6D using the standard gamble technique. Three different models were fitted for SF-6D via Bayesian Markov chain Monte Carlo (MCMC) simulation methods. These comprised a beta regression, random effects and random effects with covariates. Results from applying the three Bayesian beta regression models were reported and compared based on their predictive ability to previously used linear regression models, using mean prediction error (MPE), root mean squared error (RMSE) and deviance information criterion (DIC). : For the three different approaches, the beta regression model was found to perform better than the normal regression model under all criteria used. The beta regression with random effects model performs best, with MPE (0.084), RMSE (0.058) and DIC (-1621). Compared to the traditionally linear regression model, the beta regression provided better predictions of observed values in the entire learning sample and in an out-of-sample validation. : Beta regression provides a flexible approach to modeling health state values. It also accounted for the boundedness and heteroscedasticity of the SF-6D index scores. Further research is encouraged.
通常，与健康相关的生活质量数据建模往往很麻烦，因为其分布呈正偏态或负偏态，在零或一处有峰值，有界且具有异方差性。在本文中，我们旨在研究贝叶斯β回归是否适合分析SF - 6D健康状态效用得分和受访者特征。从贝鲁特美国大学抽取了126名黎巴嫩成员作为样本，他们使用标准博弈技术对由SF - 6D定义的49种健康状态进行了评估。通过贝叶斯马尔可夫链蒙特卡罗（MCMC）模拟方法对SF - 6D拟合了三种不同的模型。这些模型包括β回归、随机效应模型以及带协变量的随机效应模型。报告了应用这三种贝叶斯β回归模型的结果，并基于它们相对于先前使用的线性回归模型的预测能力进行比较，使用平均预测误差（MPE）、均方根误差（RMSE）和离差信息准则（DIC）。对于这三种不同的方法，发现在所有使用的标准下，β回归模型的表现均优于正态回归模型。带随机效应的β回归模型表现最佳，MPE为（0.084），RMSE为（0.058），DIC为（-1621）。与传统的线性回归模型相比，β回归在整个学习样本和样本外验证中对观测值提供了更好的预测。β回归为健康状态值建模提供了一种灵活的方法。它还考虑了SF - 6D指数得分的有界性和异方差性。鼓励进一步研究。
