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A Semiparametric Bivariate Probit Model for Joint Modeling of Outcomes in STEMI Patients

机译:STEMI患者联合建模的半甲酰均等探测模型

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In this work we analyse the relationship among in-hospital mortality and a treatment effectiveness outcome in patients affected by ST-Elevation myocardial infarction. The main idea is to carry out a joint modeling of the two outcomes applying a Semiparametric Bivariate Probit Model to dataarising from a clinical registry called STEMI Archive. A realistic quantification of the relationship between outcomes can be problematic for several reasons. First, latent factors associated with hospitals organization can affect the treatment efficacy and/or interact with patient’s condition at admission time. Moreover, they can also directly influence the mortality outcome. Such factors can be hardly measurable. Thus, the use of classical estimation methods will clearly result in inconsistent or biasedparameter estimates. Secondly, covariate-outcomes relationships can exhibit nonlinear patterns. Provided that proper statistical methods for model fitting in such framework are available, it is possible to employ a simultaneous estimation approach to account for unobservable confounders. Such a framework can also provide flexible covariate structures and model the whole conditional distribution of the response.
机译:在这项工作中,我们分析了由ST升高心肌梗死的患者的住院内死亡率和治疗效果结果的关系。主要思想是开展与称为STEMI存档的临床登记处应用半栽培生成概率模型的两种结果的联合建模。由于几个原因,结果的逼真量化可能是有问题的。首先,与医院组织相关的潜在因素可以影响治疗效果和/或在入院时间与患者的病症相互作用。此外,它们也可以直接影响死亡率结果。这些因素可能几乎无法衡量。因此,使用经典估计方法将明确地导致不一致或偏见的参数估计。其次,协变量 - 结果关系可以表现出非线性模式。如果可以使用这种框架中的模型配件的适当统计方法,可以采用同时估计方法来解释不可观察的混淆。这样的框架还可以提供灵活的协变量结构和模型响应的整个条件分布。

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