Permutation tests for joinpoint regression with applications to cancer rates

Dados Bibliográficos

ID23358075
AutoresHyune-Ju Kim (Carnegie Hall, autor correspondente), Michael P Fay (0000-0002-8643-9625, National Cancer Institute), Eric J Feuer (0000-0003-4842-7751, National Cancer Institute), Douglas N Midthune (National Cancer Institute)
Ano2000
Volume19
Fascículo3
Páginas335-351
Data de publicação2000-02-15
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoStatistics in Medicine (JOURNAL)
Identificadores do periódicoISSN: 0277-6715 • E-ISSN: 1097-0258
EditoraWiley (PUBLISHER • GB)
DOI10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z
PMID10649300
OpenAlexW2077618811
IdiomaEN
Citações recebidas549
Referências citadas24

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.

Autocorrelation · Bonferroni correction · Econometrics · Monte Carlo method · Permutation (music) · Poisson regression · Regression · Regression analysis · Statistical hypothesis testing · Statistics · Variance (accounting) · Bayesian Methods and Mixture Models · Mathematics · Medicine · Statistical Methods and Bayesian Inference · Statistical Methods and Inference