Regression analysis of clustered current status data with informative cluster size under a transformed survival model.

IF 1.2 4区数学 International Journal of Biostatistics Pub Date : 2025-03-24 eCollection Date: 2025-05-01 DOI:10.1515/ijb-2023-0130

Yanqin Feng, Shijiao Yin, Jieli Ding

引用次数: 0

Abstract

In this paper, we study inference methods for regression analysis of clustered current status data with informative cluster sizes. When the correlated failure times of interest arise from a general class of semiparametric transformation frailty models, we develop a nonparametric maximum likelihood estimation based method for regression analysis and conduct an expectation-maximization algorithm to implement it. The asymptotic properties including consistency and asymptotic normality of the proposed estimators are established. Extensive simulation studies are conducted and indicate that the proposed method works well. The developed approach is applied to analyze a real-life data set from a tumorigenicity study.

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转换生存模型下具有信息聚类大小的聚类现状数据的回归分析。

本文研究了基于信息聚类大小的聚类现状数据回归分析的推理方法。当相关失效时间来自于一类一般的半参数变换脆弱性模型时，我们开发了一种基于非参数极大似然估计的回归分析方法，并进行了期望最大化算法来实现它。建立了所提估计量的渐近性质，包括相合性和渐近正态性。大量的仿真研究表明，所提出的方法是有效的。所开发的方法被应用于分析来自致瘤性研究的真实数据集。

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.