分析心血管心力衰竭患者生存期的非混合治愈率模型

Q4 Mathematics

Model Assisted Statistics and Applications Pub Date : 2023-03-24 DOI:10.3233/mas-221341

R. P. de Oliveira, Marcos Vinicius de Oliveira Peres, Wesley Bertoli, J. Achcar

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引用次数: 0

摘要

本研究考虑基于离散Burr XIII分布的非混合模型来模拟存在固化分数的循环事件数据。在这种情况下，我们提供了一种替代标准Cox比例风险模型的方法，使用离散分布来分析假设治愈率为非混合结构的寿命数据。在贝叶斯环境中，提出的方法被考虑用于分析来自回顾性队列研究的真实数据集，该研究旨在评估影响巴基斯坦费萨拉巴德心脏病学和联合医院(2015年4月至12月)299名心力衰竭患者寿命的特定临床条件。模型验证过程使用Cox-Snell残差进行处理，这使我们能够确定所提出的非混合固化率模型的适用性。

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A non-mixture cure rate model for analyzing the survival lifetimes of cardiovascular heart failure patients

The present study considers non-mixture models based on the discrete Burr XIII distribution to model recurrent event data in the presence of a cure fraction. In this context, we provide an alternative to the standard Cox proportional hazards model using a discretized distribution to analyze lifetime data assuming a non-mixture structure for cure rates. In a Bayesian setting, the proposed methodology was considered for analyzing a real dataset from a retrospective cohort study that aimed to evaluate specific clinical conditions that affect the lifetimes of 299 heart failure patients admitted to the Institute of Cardiology and Allied Hospital – Faisalabad, Pakistan (April-December, 2015). The model validation process was addressed using the Cox-Snell residuals, which allowed us to identify the suitability of the proposed non-mixture cure rate model.

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来源期刊

Model Assisted Statistics and Applications Mathematics-Applied Mathematics

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.