Abstract
A young age at diagnosis of cancer is often seen as an indicator of the aggressiveness
of the tumor. However, empirical studies have shown conflicting results on the association
between age at diagnosis and survival. There are two choices of time scale for a Cox
regression model: time since diagnosis, and age. The regression analysis of relative
survival rates is an alternative to the Cox model. Using breast cancer data from a
population-based cancer registry, we illustrate the features of Cox models using the
two time scales and compare them with the relative survival approach. Using a Cox
model with time since diagnosis as the time scale, a younger age at diagnosis is associated
with a lower mortality; using age as the time scale gives the opposite result. The
relative survival approach agrees with the Cox model with age as the time scale. We
maintain that a careful clarification of research purpose and a careful choice of
methods are necessary.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Journal of Clinical EpidemiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Prognostic and predictive factors.in: Harris J.R. Lippman M.E. Morrow M. Osborne C.K. Diseases of the breast. 2nd ed. Wolters Kluwer;, Philadelphia2000: 489-514
- Effect of age on the survival of breast cancer patients.Eur J Cancer. 1997; 33: 425-428
- Breast cancer outcome and predictors of outcome.J Natl Cancer Inst Mongr. 1994; 16: 35-42
- Relationship of patient age to pathologic features of the tumor and prognosis for patients with stage I or II breast cancer.J Clin Oncol. 1994; 12: 888-894
- Racial differences in breast carcinoma survival.Cancer. 2000; 88: 114-123
- Regression models and life tables (with discussion).J R Stat Soc B. 1972; 34: 187-220
- Survival analysis using the SAS system. SAS Institute Inc.;, Cary, NC1995
- Time-to-event analysis of longitudinal follow-up of a survey.Am J Epidemiol. 1997; 145: 72-80
- Regression analysis of relative survival rates.Appl Stat. 1987; 36: 309-317
- SURV2. Finnish Cancer Registry;, Helsinki2000
- U.S. decennial life tables for 1989–1991, vol 1 no 1. National Center for Health Statistics;, Hyattsville, MD1997
National Cancer Institute. SEER cancer incidence public-use database, 1973–1997. National Cancer Institute CD-ROM # 2, ASCII version.
- Ries L.A. Kosary C.L. Hankey B.F. Harras A. Miller B.A. Edwards B.K. SEER cancer statistics. review, 1973–1994. National Cancer Institute;, Bethesda, MD1997
- Statistical models in epidemiology. Oxford University Press;, Oxford1993
- The effect of age and comorbidity in the treatment of elderly women with nonmetastatic breast cancer.Arch Intern Med. 1996; 156: 85-90
StataCorp. Stata reference manual. Release 6. College Station, TX: Stata Press; 2001.
- Political generation and partisanship in the UK, 1964–1997.J R Stat Soc Series A. 2002; 165: 121-135
- The long-term pattern of adult mortality and the highest attained age.J R Stat Soc Series A. 1999; 162: 5-43
Article info
Publication history
Accepted:
September 2,
2002
Received in revised form:
April 30,
2002
Received:
January 14,
2002
Identification
Copyright
© 2003 Elsevier Science Inc. Published by Elsevier Inc. All rights reserved.
