A First Passage Time Model for Long-Term Survivors with Competing Risks

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-05-21 DOI:10.2202/1557-4679.1224

Ruimin Xu, P. McNicholas, A. Desmond, G. Darlington

引用次数: 5

Abstract

We investigate a competing risks model, using the specification of the Gompertz distribution for failure times from competing causes and the inverse Gaussian distribution for failure times from the cause of interest. The expectation-maximization algorithm is used for parameter estimation and the model is applied to real data on breast cancer and melanoma. In these applications, our models compare favourably with existing techniques. The proposed method provides a useful technique that may be more broadly applicable than existing alternatives.

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具有竞争风险的长期幸存者的首次通过时间模型

我们研究了一个竞争风险模型，使用来自竞争原因的失败次数的Gompertz分布和来自利益原因的失败次数的逆高斯分布的规范。采用期望最大化算法进行参数估计，并将该模型应用于乳腺癌和黑色素瘤的实际数据。在这些应用中，我们的模型与现有技术相比具有优势。所提出的方法提供了一种有用的技术，可能比现有的替代方法更广泛地适用。

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.