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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Kim HJ,Fay MP,Feuer EJ,Midthune DNdoi
10.1002/(sici)1097-0258(20000215)19:3<335::aid-simsubject
Has Abstractpub_date
2000-02-15 00:00:00pages
335-51issue
3eissn
0277-6715issn
1097-0258pii
10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIMjournal_volume
19pub_type
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