Literature DB >> 11916552

A computer program for period analysis of cancer patient survival.

H Brenner1, O Gefeller, T Hakulinen.   

Abstract

Monitoring of long-term survival rates, which is now routinely performed by many cancer registries throughout the world, should be as up-to-date as possible. A few years ago, a new method of survival analysis, denoted period analysis, has been proposed which provides more up-to-date estimates of long-term survival rates than traditional survival analysis by exclusively reflecting the survival experience of patients within a recent calendar period. However, application of this method has so far been hindered by the lack of pertinent computer programs. In this paper, we present a simple and easy-to-use computer program (SAS macro) that enables one to carry out period analysis (as well as conventional analysis) of both absolute and relative survival rates with the type of data commonly available in population-based cancer registries. We illustrate application of the program with examples from the nationwide Finnish Cancer Registry.

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Year:  2002        PMID: 11916552     DOI: 10.1016/s0959-8049(02)00003-5

Source DB:  PubMed          Journal:  Eur J Cancer        ISSN: 0959-8049            Impact factor:   9.162


  20 in total

1.  Survival of patients with lymphoplasmacytic lymphoma and solitary plasmacytoma in Germany and the United States of America in the early 21st century.

Authors:  Janick Weberpals; Dianne Pulte; Lina Jansen; Sabine Luttmann; Bernd Holleczek; Alice Nennecke; Meike Ressing; Alexander Katalinic; Maximilian Merz; Hermann Brenner
Journal:  Haematologica       Date:  2017-03-09       Impact factor: 9.941

2.  Survival after a diagnosis of testicular germ cell cancers in Germany and the United States, 2002-2006: a high resolution study by histology and age.

Authors:  A Stang; L Jansen; B Trabert; C Rusner; A Eberle; A Katalinic; K Emrich; B Holleczek; H Brenner
Journal:  Cancer Epidemiol       Date:  2013-04-23       Impact factor: 2.984

3.  Long-term survival of patients with small intestinal carcinoid tumors.

Authors:  Niklas Zar; Hans Garmo; Lars Holmberg; Jonas Rastad; Per Hellman
Journal:  World J Surg       Date:  2004-11       Impact factor: 3.352

4.  Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data.

Authors:  Juhyeon Kim; Hyunjung Shin
Journal:  J Am Med Inform Assoc       Date:  2013-03-06       Impact factor: 4.497

5.  Survival from colorectal cancer in Germany in the early 21st century.

Authors:  O Majek; A Gondos; L Jansen; K Emrich; B Holleczek; A Katalinic; A Nennecke; A Eberle; H Brenner
Journal:  Br J Cancer       Date:  2012-05-03       Impact factor: 7.640

6.  Extension of cox proportional hazard model for estimation of interrelated age-period-cohort effects on cancer survival.

Authors:  Tengiz Mdzinarishvili; Michael X Gleason; Leo Kinarsky; Simon Sherman
Journal:  Cancer Inform       Date:  2011-02-23

7.  Standard errors of non-standardised and age-standardised relative survival of cancer patients.

Authors:  L Jansen; T Hakulinen; H Brenner
Journal:  Br J Cancer       Date:  2011-12-15       Impact factor: 7.640

8.  Survival of endometrial cancer patients in Germany in the early 21st century: a period analysis by age, histology, and stage.

Authors:  Tianhui Chen; Lina Jansen; Adam Gondos; Meike Ressing; Bernd Holleczek; Alexander Katalinic; Hermann Brenner
Journal:  BMC Cancer       Date:  2012-03-30       Impact factor: 4.430

9.  Survival of patients with oral cavity cancer in Germany.

Authors:  Stefan Listl; Lina Jansen; Albrecht Stenzinger; Kolja Freier; Katharina Emrich; Bernd Holleczek; Alexander Katalinic; Adam Gondos; Hermann Brenner
Journal:  PLoS One       Date:  2013-01-18       Impact factor: 3.240

10.  Study populations for period analyses of cancer survival.

Authors:  L Jansen; T Hakulinen; H Brenner
Journal:  Br J Cancer       Date:  2013-01-29       Impact factor: 7.640

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