Permutation tests for joinpoint regression with applications to cancer rates

TLDR

The identification of changes in recent cancer mortality and incidence trends is a key analytical concern. The study applies a joinpoint regression model to describe continuous changes in cancer trend data. The authors fit the model using a grid‑search approach, perform permutation tests with Monte Carlo p‑values and Bonferroni correction, and extend the method to handle non‑constant variance, Poisson variation, and autocorrelated errors. Simulations and application to U.S. prostate cancer incidence and mortality rates demonstrate the performance of the proposed tests.

Abstract

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.