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首页> 外文期刊>Journal of the Royal Statistical Society. Series C, Applied statistics >Bayesian model selection for join point regression with application to age-adjusted cancer rates
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Bayesian model selection for join point regression with application to age-adjusted cancer rates

机译：用于连接点回归的贝叶斯模型选择及其在年龄调整后的癌症发生率中的应用

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摘要

The method of Bayesian model selection for join point regression models is developed. Given a set of K + 1 join point models M_0, M_1,...,M_K with 0,1.....K join points respectively, the posterior distributions of the parameters and competing models M_k are computed by Markov chain Monte Carlo simulations. The Bayes information criterion BIC is used to select the model M_k with the smallest value of BIC as the best model. Another approach based on the Bayes factor selects the model M_k with the largest posterior probability as the best model when the prior distribution of M_k is discrete uniform. Both methods are applied to analyse the observed US cancer incidence rates for some selected cancer sites. The graphs of the join point models fitted to the data are produced by using the methods proposed and compared with the method of Kim and co-workers that is based on a series of permutation tests. The analyses show that the Bayes factor is sensitive to the prior specification of the variance σ~2, and that the model which is selected by BIC fits the data as well as the model that is selected by the permutation test and has the advantage of producing the posterior distribution for the join points. The Bayesian join point model and model selection method that are presented here will be integrated in the National Cancer Institute's join point software (http://www.srab.cancer.gov/joinpoint/) and will be available to the public.
机译：提出了连接点回归模型的贝叶斯模型选择方法。给定分别具有0,1 ..... K个连接点的一组K + 1个连接点模型M_0，M_1，...，M_K，则参数和竞争模型M_k的后验分布是通过马尔可夫链蒙特卡洛计算的模拟。使用贝叶斯信息准则BIC选择具有最小BIC值的模型M_k作为最佳模型。当M_k的先验分布是离散均匀时，基于贝叶斯因子的另一种方法选择具有最大后验概率的模型M_k作为最佳模型。两种方法都适用于分析某些选定癌症部位观察到的美国癌症发病率。拟合数据的连接点模型的图形是通过使用建议的方法生成的，并与基于一系列置换测试的Kim和同事的方法进行了比较。分析表明，贝叶斯因子对方差σ〜2的先验规范敏感，并且由BIC选择的模型与通过排列检验选择的模型一样适合数据，并且具有产生数据的优势连接点的后验分布。此处介绍的贝叶斯连接点模型和模型选择方法将集成到美国国家癌症研究所的连接点软件（http://www.srab.cancer.gov/joinpoint/）中，并向公众开放。

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