Copula Approach for Developing a Biomarker Panel for Prediction of Dengue Hemorrhagic Fever
Copula Approach for Developing a Biomarker Panel for Prediction of Dengue Hemorrhagic Fever
复制标题
DOI：
10.1007/s40745-020-00293-x
复制
发表时间：
2020-06
影响因子：
--
通讯作者：
Jong-Min Kim;H. Ju;Yoonsung Jung
中科院分区：
文献类型：
--
作者：
Jong-Min Kim;H. Ju;Yoonsung Jung
选择变量选择方法来识别二元分类建模的重要变量对于生成可解释、生成准确预测且偏差最小的稳定统计模型至关重要。这项工作的动机是从参与一项急性人类登革热感染前瞻性观察研究的 51 名个体中获得登革热感染的临床和实验室特征数据。我们的论文使用客观贝叶斯方法来识别登革热数据集上登革出血热（DHF）的重要变量。通过客观贝叶斯方法选择重要变量，采用考虑相关误差结构的高斯 copula 边际回归模型和高斯 copula 模型半参数贝叶斯推理的一般方法，分别估计边际分布和依赖结构。我们还对 DHF 预测模型进行了受试者工作特征 (ROC) 分析，并将我们提出的模型与 Ju 和 Brasier 的其他模型（用于开发登革出血热预测的生物标志物组的变量选择方法。BMC Res Notes 6:365, 2013）在 ROC 分析的基础上进行了测试。我们的结果扩展了之前的 DHF 模型，表明 IL-10、发烧天数、性和淋巴细胞是基于血液化学和细胞因子测量预测 DHF 的主要特征。此外，通过半参数贝叶斯高斯关联模型和高斯偏相关方法发现了这些天数发烧、淋巴细胞、IL-10和性蛋白谱与疾病结果相关的依赖性结构。
The choice of variable-selection methods to identify important variables for binary classification modeling is critical for producing stable statistical models that are interpretable, that generate accurate predictions, and have minimal bias. This work is motivated by the availability of data on clinical and laboratory features of dengue fever infections obtained from 51 individuals enrolled in a prospective observational study of acute human dengue infections. Our paper uses objective Bayesian method to identify important variables for dengue hemorrhagic fever (DHF) over the dengue data set. With the selected important variables by objective Bayesian method, we employ a Gaussian copula marginal regression model considering correlation error structure and a general method of semi-parametric Bayesian inference for Gaussian copula model to estimate, separately, the marginal distribution and dependence structure. We also carry out a receiver operating characteristic (ROC) analysis for the predictive model for DHF and compare our proposed model with the other models of Ju and Brasier (Variable selection methods for developing a biomarker panel for prediction of dengue hemorrhagic fever. BMC Res Notes 6:365, 2013) tested on the basis of the ROC analysis. Our results extend the previous models of DHF by suggesting that IL-10, Days Fever, Sex and Lymphocytes are the major features for predicting DHF on the basis of blood chemistries and cytokine measurements. In addition, the dependence structure of these Days Fever, Lymphocytes, IL-10 and Sex protein profiles associated with disease outcomes was discovered by the semi-parametric Bayesian Gaussian copula model and Gaussian partial correlation method.