815
views
0
recommends
+1 Recommend
0 collections
    5
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Permutation tests for joinpoint regression with applications to cancer rates.

      Statistics in Medicine

      epidemiology, Algorithms, Humans, Incidence, Male, Monte Carlo Method, Poisson Distribution, Prostatic Neoplasms, Regression Analysis, United States

      Read this article at

      ScienceOpenPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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 2000 John Wiley & Sons, Ltd.

          Related collections

          Author and article information

          Journal
          10649300

          Comments

          Comment on this article