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Showing content from https://pubmed.ncbi.nlm.nih.gov/10649300/ below:

Permutation tests for joinpoint regression with applications to cancer rates

. 2000 Feb 15;19(3):335-51. doi: 10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z. Permutation tests for joinpoint regression with applications to cancer rates

Affiliations

Affiliation

¹ Syracuse University, Department of Mathematics, 215 Carnegie Building, Syracuse University, Syracuse, NY 13244-1150, USA. hjkim@mailbox.syr.edu

PMID: 10649300
DOI: 10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z

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Permutation tests for joinpoint regression with applications to cancer rates

H J Kim et al. Stat Med. 2000.

. 2000 Feb 15;19(3):335-51. doi: 10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z. Affiliation

¹ Syracuse University, Department of Mathematics, 215 Carnegie Building, Syracuse University, Syracuse, NY 13244-1150, USA. hjkim@mailbox.syr.edu

PMID: 10649300
DOI: 10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z

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Erratum in

Stat Med 2001 Feb 28;20(4):655

Abstract

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.

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