Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival
Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival
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DOI：
10.1111/rssc.12021
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发表时间：
2014-01-01
影响因子：
1.6
通讯作者：
Soriano, Jacopo
中科院分区：
文献类型：
--
作者：
Guglielmi, Alessandra;Ieva, Francesca;Soriano, Jacopo
贝叶斯半参数Logit模型适用于与ST段抬高心肌梗死诊断住院患者的住院生存结果相关的分组数据。考虑相依Dirichlet过程先验，对分组因素(入院医院)的随机效应分布进行建模，以提供医院的聚类分析。通过最小化适当损失函数的后验期望值的最优随机划分来突出聚类结构。这项工作有两个主要目标：根据提供者对患者预后影响的相似性，提供基于模型的聚类和排名，以及在患者层面对生存结果做出可靠预测，即使存活率本身严重失衡。这项研究是在一个名为伦巴第大区战略计划的项目中进行的，旨在支持医疗保健政策的决策。
Bayesian semiparametric logit models are fitted to grouped data related to in-hospital survival outcome of patients hospitalized with an ST-segment elevation myocardial infarction diagnosis. Dependent Dirichlet process priors are considered for modelling the random-effects distribution of the grouping factor (hospital of admission), to provide a cluster analysis of the hospitals. The clustering structure is highlighted through the optimal random partition that minimizes the posterior expected value of a suitable loss function. There are two main goals of the work: to provide model-based clustering and ranking of the providers according to the similarity of their effect on patients' outcomes, and to make reliable predictions on the survival outcome at the patient's level, even when the survival rate itself is strongly unbalanced. The study is within a project, named the Strategic program of Regione Lombardia', and is aimed at supporting decisions in healthcare policies.