Permutation tests for joinpoint regression with applications to cancer rates

Hyune Ju Kim, Michael P. Fay, Eric J. Feuer, Douglas N. Midthune

Research output: Contribution to journalArticlepeer-review

2347 Scopus citations

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. Copyright (C) 2000 John Wiley and Sons, Ltd.

Original languageEnglish (US)
Pages (from-to)335-351
Number of pages17
JournalStatistics in Medicine
Volume19
Issue number3
DOIs
StatePublished - Feb 15 2000

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Permutation tests for joinpoint regression with applications to cancer rates'. Together they form a unique fingerprint.

Cite this