Permutation tests for joinpoint regression with applications to cancer rates
Permutation tests for joinpoint regression with applications to cancer rates
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DOI：
10.1002/(sici)1097-0258(20000215)19:3
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发表时间：
2000-02-15
影响因子：
2
通讯作者：
Midthune, DN
中科院分区：
文献类型：
--
作者：
Kim, HJ;Fay, MP;Midthune, DN
确定最近趋势的变化是分析癌症死亡率和发病率数据的一个重要问题。我们应用一个连接点回归模型来描述这种连续的变化，并使用网格搜索方法来拟合回归函数与未知的连接点假设恒定的方差和不相关的误差。我们发现显着的连接点的数量，通过执行几个排列测试，其中每个都有一个正确的显着性水平渐近。使用Monte Carlo方法找到每个p值，并通过Bonferroni校正保持总体渐近显著性水平。这些测试扩展到非恒定方差的情况下，处理率泊松变化和可能的自相关误差。通过模拟研究这些测试的性能，并将测试应用于美国前列腺癌的发病率和死亡率。版权所有（C）2000约翰威利父子有限公司
The identification of changes in the recent trend is an important issue in the analysis of cancer mortality and incidence data. We apply a joinpoint regression model to describe such continuous changes and use the grid-search method to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors. We find the number of significant joinpoints by performing several permutation tests, each of which has a correct significance level asymptotically. Each p-value is found using Monte Carlo methods, and the overall asymptotic significance level is maintained through a Bonferroni correction. These tests are extended to the situation with non-constant variance to handle rates with Poisson variation and possibly autocorrelated errors. The performance of these tests are studied via simulations and the tests are applied to U.S. prostate cancer incidence and mortality rates. Copyright (C) 2000 John Wiley & Sons, Ltd.