多变量间隔截短失效时间数据的回归分析及其在致瘤性实验中的应用

IF 1.8 3区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Biometrical Journal Pub Date : 2008-06-06 DOI:10.1002/bimj.200710418

Xingwei Tong, Man-Hua Chen, Jianguo Sun

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引用次数: 21

摘要

本文讨论了当存在多个相关生存时间且每个生存时间只有间隔审查数据时发生的多变量间隔审查失效时间数据。这样的数据出现在许多领域。一种是致瘤性实验，通常涉及不同类型的肿瘤，肿瘤发生在动物的不同部位，或同时发生。为了对这些数据进行回归分析，我们使用加性风险模型开发了一种边际推断方法，并将其应用于一组来自致瘤性实验的双变量区间截尾数据。仿真研究对所提出的方法进行了评估，并表明该方法在实际情况下表现良好。(©2008 WILEY-VCH Verlag GmbH &KGaA公司，Weinheim)

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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Regression Analysis of Multivariate Interval-Censored Failure Time Data with Application to Tumorigenicity Experiments

This paper discusses multivariate interval-censored failure time data that occur when there exist several correlated survival times of interest and only interval-censored data are available for each survival time. Such data occur in many fields. One is tumorigenicity experiments, which usually concern different types of tumors, tumors occurring in different locations of animals, or together. For regression analysis of such data, we develop a marginal inference approach using the additive hazards model and apply it to a set of bivariate interval-censored data arising from a tumorigenicity experiment. Simulation studies are conducted for the evaluation of the presented approach and suggest that the approach performs well for practical situations. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

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来源期刊

Biometrical Journal 生物-数学与计算生物学

CiteScore

3.20

自引率

5.90%

发文量

119

审稿时长

6-12 weeks

期刊介绍： Biometrical Journal publishes papers on statistical methods and their applications in life sciences including medicine, environmental sciences and agriculture. Methodological developments should be motivated by an interesting and relevant problem from these areas. Ideally the manuscript should include a description of the problem and a section detailing the application of the new methodology to the problem. Case studies, review articles and letters to the editors are also welcome. Papers containing only extensive mathematical theory are not suitable for publication in Biometrical Journal.