Estimating subject-specific hazard functions

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-04-24 DOI:10.1093/jrsssc/qlad030

Moumita Chatterjee, B. Ganguli, Sugata Sen Roy

引用次数: 0

Abstract

The central idea of this paper is to compare mean responses of several subjects in the presence of censoring and subject-specific variation. We develop a semiparametric mixed model for fitting subject-specific hazard curves to a set of censored failure times. A spline-based model and a mixed effects framework for smoothing are used. Efficient estimators of fixed parameters and predictors of the random components are derived and their asymptotic properties studied. This is a generalization of the method proposed by [Cai, T., Hyndman, R. J., & Wand, M. P. (2002). Mixed model-based hazard estimation. Journal of Computational and Graphical Statistics, 11(4), 784–798. https://doi.org/10.1198/106186002862] to incorporate additional subject-specific variation of the hazard function. The results are illustrated using two motivating examples.

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估计特定主题的危害函数

本文的中心思想是比较几个受试者在审查和受试者特定变化的情况下的平均反应。我们开发了一个半参数混合模型，用于拟合主题特定的危险曲线到一组截尾失效时间。使用基于样条的模型和混合效果框架进行平滑。导出了固定参数的有效估计量和随机分量的有效预测量，并研究了它们的渐近性质。这是对Cai, T.， Hyndman, R. J, and Wand, M. P.(2002)提出的方法的推广。基于混合模型的危害估计。计算与图形统计，11(4)，784-798。https://doi.org/10.1198/106186002862]以纳入额外的针对特定主题的危险函数变化。用两个实例说明了结果。

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

Journal of the Royal Statistical Society Series C-Applied Statistics 数学-统计学与概率论

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.