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致癌作用随机模型的拟合优度检验。

Testing goodness of fit for stochastic models of carcinogenesis.

作者信息

Gregori Giovanni, Hanin Leonid, Luebeck Georg, Moolgavkar Suresh, Yakovlev Andrei

机构信息

Department of Oncological Sciences, Division of Biostatistics, Huntsman Cancer Institute, University of Utah, 2000 Circle of Hope, Salt Lake City, UT 84112, USA.

出版信息

Math Biosci. 2002 Jan;175(1):13-29. doi: 10.1016/s0025-5564(01)00088-8.

Abstract

This paper considers the utility of statistical goodness of fit testing in the context of mechanistic models of carcinogenesis. Two stochastic models of carcinogenesis were tested with several sets of experimental and epidemiological data using a formal goodness of fit test specially designed to accommodate censored observations: these were the two-stage model allowing for clonal expansion of initiated cells and its simpler version with gamma distributed promotion time. The results of this application, supplemented by visual examination of local likelihood kernel estimates of the hazard function and the corresponding model-based estimates, show that mechanistic models of carcinogenesis provide a good fit to the data in the majority of cases under study.

摘要

本文探讨了在癌症发生机制模型背景下统计拟合优度检验的效用。使用专门设计用于处理删失观测值的形式拟合优度检验,对两个癌症发生随机模型进行了几组实验和流行病学数据测试:一个是允许起始细胞克隆扩增的两阶段模型,另一个是促进时间呈伽马分布的更简单版本。该应用结果,再辅以对风险函数的局部似然核估计和相应基于模型的估计的直观检查,表明在大多数研究案例中,癌症发生机制模型与数据拟合良好。

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