综合右删失和长度偏右删失故障时间数据的生存函数 NPMLE：特性与应用

IF 1 4区数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Biostatistics Pub Date : 2024-04-09 DOI:10.1515/ijb-2023-0121

James H. McVittie, David B. Wolfson, David A. Stephens

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引用次数: 0

摘要

在生存分析中，许多队列研究都包含了由事故病例和流行病例组成的子队列。与单独分析事件队列和流行队列的数据相比，将这两个子队列的数据结合起来肯定有其优势。在本文中，我们讨论了使用长度偏右删失流行队列数据和右删失事件队列数据的生存函数非参数极大似然估计法（NPMLE）。我们建立了生存函数 NPMLE 的渐近特性，并利用 NPMLE 估算了在蒙特利尔地区医院花费时间的分布。

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The survival function NPMLE for combined right-censored and length-biased right-censored failure time data: properties and applications

Many cohort studies in survival analysis have imbedded in them subcohorts consisting of incident cases and prevalent cases. Instead of analysing the data from the incident and prevalent cohorts alone, there are surely advantages to combining the data from these two subcohorts. In this paper, we discuss a survival function nonparametric maximum likelihood estimator (NPMLE) using both length-biased right-censored prevalent cohort data and right-censored incident cohort data. We establish the asymptotic properties of the survival function NPMLE and utilize the NPMLE to estimate the distribution for time spent in a Montreal area hospital.

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来源期刊

International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY

CiteScore

2.10

自引率

8.30%

发文量

审稿时长

>12 weeks

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.