DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data.

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-02-04 DOI:10.1515/ijb-2024-0042

Haixiang Zhang, Yang Li, HaiYing Wang

引用次数: 0

Abstract

To ensure privacy protection and alleviate computational burden, we propose a fast subsmaling procedure for the Cox model with massive survival datasets from multi-centered, decentralized sources. The proposed estimator is computed based on optimal subsampling probabilities that we derived and enables transmission of subsample-based summary level statistics between different storage sites with only one round of communication. For inference, the asymptotic properties of the proposed estimator were rigorously established. An extensive simulation study demonstrated that the proposed approach is effective. The methodology was applied to analyze a large dataset from the U.S. airlines.

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DsubCox：一种针对分布式海量生存数据的Cox模型的快速子采样算法。

为了保证隐私保护和减轻计算负担，我们提出了一种基于多中心、分散来源的大量生存数据集的Cox模型的快速子化过程。所提出的估计器是基于我们导出的最优子抽样概率计算的，并且只需要一轮通信就可以在不同的存储站点之间传输基于子抽样的汇总级统计信息。对于推理，严格地建立了所提估计量的渐近性质。大量的仿真研究表明，该方法是有效的。该方法被用于分析来自美国航空公司的大型数据集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.