Efficient regularized estimation of graphical proportional hazards model with interval-censored data

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Statistics & Data Analysis Pub Date : 2025-03-27 DOI:10.1016/j.csda.2025.108178

Huimin Lu , Yilong Wang , Heming Bing , Shuying Wang , Niya Li

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引用次数: 0

Abstract

Variable selection is discussed in many cases in survival analysis. In particular, the analysis of using proportional hazards (PH) models to deal with censored survival data has established a large amount of literature. Based on interval-censored data, this paper discusses the situation of complex network structures existing in covariates. To address the issue, a more flexible and versatile PH model has been developed by combining probabilistic graphical models with PH models, to describe the correlation between covariates. Based on the block coordinate descent method, a penalized estimation method is proposed, which can simultaneously perform variable selection and parameter estimation. The effectiveness of the proposed model and its parameter estimation method are evaluated through simulation studies and the analysis of clinical trial data related to Alzheimer's disease, confirming the reliability and accuracy of the proposed model and method.

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区间截尾数据下图形比例风险模型的有效正则化估计

在生存分析中，很多情况下都要讨论变量选择。特别是，使用比例风险（PH）模型处理审查生存数据的分析已经建立了大量的文献。基于区间截尾数据，讨论了协变量中存在复杂网络结构的情况。为了解决这个问题，通过将概率图模型与PH模型相结合，开发了一个更灵活、更通用的PH模型，以描述协变量之间的相关性。在块坐标下降法的基础上，提出了一种同时进行变量选择和参数估计的惩罚估计方法。通过仿真研究和对阿尔茨海默病相关临床试验数据的分析，对所提模型及其参数估计方法的有效性进行了评价，证实了所提模型和方法的可靠性和准确性。

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来源期刊

Computational Statistics & Data Analysis 数学-计算机：跨学科应用

CiteScore

3.70

自引率

5.60%

发文量

167

审稿时长

60 days

期刊介绍： Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]