重叠指数的推理:与癌症数据的应用。

IF 1.2 4区 数学
Raju Dey, Arne C Bathke, Somesh Kumar
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引用次数: 0

摘要

两种分布之间重叠的量化在生物学、医学、遗传学和生态学研究的各个领域都有应用。本文考虑了新的重叠指数和遏制指数来量化两个物种/种群之间的生态位重叠。建立了这些指标的一些新性质,并研究了两种分布在不同尺度参数下呈指数分布时的估计问题。我们提出了几种估计器,并比较了它们相对于不同损失函数的相对性能。在一定条件下,证明了这些指标的极大似然估计的渐近正态性。我们还基于三种不同的方法获得了指数的置信区间,并比较了它们的平均长度和覆盖概率。本文提出的点和置信区间程序应用于乳腺癌数据集,以分析接受两种不同类型手术的患者生存时间之间的相似性。此外,还研究了两组患者无复发时间的相似性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inference on overlap index: with an application to cancer data.

The quantification of overlap between two distributions has applications in various fields of biology, medical, genetic, and ecological research. In this article, new overlap and containment indices are considered for quantifying the niche overlap between two species/populations. Some new properties of these indices are established and the problem of estimation is studied, when the two distributions are exponential with different scale parameters. We propose several estimators and compare their relative performance with respect to different loss functions. The asymptotic normality of the maximum likelihood estimators of these indices is proved under certain conditions. We also obtain confidence intervals of the indices based on three different approaches and compare their average lengths and coverage probabilities. The point and confidence interval procedures developed here are applied on a breast cancer data set to analyze the similarity between the survival times of patients undergoing two different types of surgery. Additionally, the similarity between the relapse free times of these two sets of patients is also studied.

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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
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.
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