Abstract
Background
A new net survival method has been introduced by Pohar Perme et al. (2012 [4]) and
recommended to substitute the relative survival methods in current use for evaluating
population-based cancer survival.
Methods
The new method is based on the use of continuous follow-up time, and is unbiased only
under non-informative censoring of the observed survival. However, the population-based
cancer survival is often evaluated based on annually or monthly tabulated follow-up
intervals. An empirical investigation based on data from the Finnish Cancer Registry
was made into the practical importance of the censoring and the level of data tabulation.
A systematic comparison was made against the earlier recommended Ederer II method
of relative survival using the two currently available computer programs (Pohar Perme
(2013) [10] and Dickman et al. (2013) [11]).
Results
With exact or monthly tabulated data, the Pohar-Perme and the Ederer II methods give,
on average, results that are at five years of follow-up less than 0.5% units and at
10 and 14 years 1–2% units apart from each other. The Pohar-Perme net survival estimator is
prone to random variation and may result in biased estimates when exact follow-up
times are not available or follow-up is incomplete. With annually tabulated follow-up
times, estimates can deviate substantially from those based on more accurate observations,
if the actuarial approach is not used.
Conclusion
At 5 years, both the methods perform well. In longer follow-up, the Pohar-Perme estimates
should be interpreted with caution using error margins. The actuarial approach should
be preferred, if data are annually tabulated.
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 European Journal of CancerAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Ries L.A.G. Melbert D. Krapcho M. SEER cancer statistics review, 1975–2004. Bethesda, MD, National Cancer Institute2007
- Cancer survival in five continents: a worldwide population-based study (CONCORD).Lancet Oncol. 2008; 9: 730-756
- Choosing the relative survival method for cancer survival estimation.Eur J Cancer. 2011; 47: 2202-2210
- On estimation in relative survival.Biometrics. 2012; 68: 113-120
- Cancer survival corrected for heterogeneity in patient withdrawal.Biometrics. 1982; 38: 933-942
Ederer F, Heise H. Instructions to IBM 650 programmers in processing survival computations. Methodological note no. 10. Bethesda, MD: End Results Evaluation Section, National Cancer Institute; 1959.
- Cancer net survival on registry data: use of the new unbiased Pohar-Perme estimator and magnitude of the bias with the classical methods.Int J Cancer. 2013; 132: 2359-2369
- Interpreting trends in cancer patient survival.J Intern Med. 2006; 260: 103-117
- How to interpret the relative survival ratios of cancer patients.Eur J Cancer. 2008; 44: 2661-2667
Pohar Perme M. relsurv: Relative survival. R package version 2.0-4; 2013. Available from: http://CRAN.R-project.org/package=relsurv. [Accessed on 26 June 2013].
Dickman PW, Coviello E, Hills M. STATA computer program ‘strs.ado’, version 1.3.8 (29 March 2013); 2013. Available from: http://www.pauldickman.com/rsmodel/stata_colon. [Accessed on 26 June 2013].
- Discussion on the paper by D.R. Cox.J R Stat Soc Ser B. 1972; 34: 216-217
- Estimating net survival; the importance of allowing for informative censoring.Stat Med. 2012; 31: 75-86
- Informative censoring in relative survival.Stat Med. 2013; https://doi.org/10.1002/sim.5877
- Information recovery and bias adjustment in proportional hazards regression analysis of randomized trials using surrogate markers.in: Proceedings of the biopharmaceutical section. American Statistical Association, San Francisco, CA1993: 24-33
- On long-term relative survival rates.J Chron Dis. 1977; 30: 431-443
- Regression models for relative survival.Stat Med. 2004; 23: 51-64
- Flexible parametric models for relative survival with application in coronary heart disease.Stat Med. 2007; 26: 5486-5498
- An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies.Stat Med. 2007; 26: 2214-2228
Article info
Publication history
Published online: October 31, 2013
Identification
Copyright
© 2013 Elsevier Ltd. Published by Elsevier Inc. All rights reserved.
