利用马尔可夫链蒙特卡洛模拟分析广义修正朗肯评分数据集

Pranjal Kumar Pandey, P. Dev, Akanksha Gupta, Abhishek Pathak, V.K. Shukla, S.K. Upadhyay
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引用次数: 0

摘要

脑出血和脑卒中是严重的医疗状况,会对人的整体健康造成破坏性影响,并受到多种因素的影响。我们经常会遇到这样的情况,尤其是在医疗领域,一个变量与多个其他特征相关联。由于其复杂性,要可视化这类具有较多特征的数据集是一项挑战。此外,由于特征之间存在较强的相关结构,因此很难通过常规的统计程序来确定有影响的变量。本文采用马尔科夫链蒙特卡罗模拟法,在贝叶斯框架内使用逻辑回归模型分析了现实生活中广泛的修正兰金评分数据集。最后,利用基于贝叶斯信息标准的模型比较工具,对模型中的多个协变量进行零检验,以简化模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Wide Modified Rankin Score Dataset using Markov Chain Monte Carlo Simulation
Brain hemorrhage and strokes are serious medical conditions that can have devastating effects on a person's overall well-being and are influenced by several factors. We often encounter such scenarios specially in medical field where a single variable is associated with several other features. Visualizing such datasets with a higher number of features poses a challenge due to their complexity. Additionally, the presence of a strong correlation structure among the features makes it hard to determine the impactful variables with the usual statistical procedure. The present paper deals with analysing real life wide Modified Rankin Score dataset within a Bayesian framework using a logistic regression model by employing Markov chain Monte Carlo simulation. Latterly, multiple covariates in the model are subject to testing against zero in order to simplify the model by utilizing a model comparison tool based on Bayes Information Criterion.
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