Permutation tests for joinpoint regression with applications to cancer rates
Article first published online: 25 JAN 2000
Copyright © 2000 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 19, Issue 3, pages 335–351, 15 February 2000
How to Cite
Kim, H.-J., Fay, M. P., Feuer, E. J. and Midthune, D. N. (2000), Permutation tests for joinpoint regression with applications to cancer rates. Statist. Med., 19: 335–351. doi: 10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-Z
- Issue published online: 25 JAN 2000
- Article first published online: 25 JAN 2000
- Manuscript Accepted: APR 1999
- Manuscript Received: APR 1998
A Correction has been published for this article in Statistics in Medicine 2001; 20:655.
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 © 2000 John Wiley & Sons, Ltd.