Literature DB >> 18819791

How to interpret the relative survival ratios of cancer patients.

Arun Pokhrel1, Timo Hakulinen.   

Abstract

It is common in population-based cancer registries to use the relative survival ratio to estimate patients' probabilities of surviving if their cancer were the only cause of death. Results from the recently proposed new methods of age-standardisation can be interpreted as ratios between the observed and expected survival proportions. Like the non-standardised ratios, these age-standardised relative survival ratios have, however, the desired probability interpretation only under a specific condition. The condition involved is the survival with respect to other causes up to the given point of follow-up. With different lengths of follow-up, this condition is also different. As a consequence, the non-standardised relative survival ratios and those standardised with the two newest methods produce, for different lengths of follow-up, mutually incomparable estimates with respect to age. Not accounting for this may, for example, lead to erroneous conclusions about the cure of the patients. The traditional method of age-standardisation does not have this problem of incomparability. Results of relative survival analyses of data from the Finnish Cancer Registry are used to illustrate this issue. To avoid overinterpretation and confusion, the different interpretations of the relative survival ratios, both non-standardised and age-standardised, must be known. For example, the very popular cumulative relative survival curves, consisting of consecutive cumulative relative survival ratios, should not be produced for the non-standardised ratios or for ratios age-standardised with the two newest methods. In practical applications, it is crucial to know which method of standardisation, and not only which standard population, has been in use.

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Year:  2008        PMID: 18819791     DOI: 10.1016/j.ejca.2008.08.016

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


  8 in total

Review 1.  Explanations for worsening cancer survival.

Authors:  Esther de Vries; Henrike E Karim-Kos; Maryska L G Janssen-Heijnen; Isabelle Soerjomataram; Lambertus A Kiemeney; Jan Willem W Coebergh
Journal:  Nat Rev Clin Oncol       Date:  2010-01       Impact factor: 66.675

2.  Education, survival and avoidable deaths in cancer patients in Finland.

Authors:  A Pokhrel; P Martikainen; E Pukkala; M Rautalahti; K Seppä; T Hakulinen
Journal:  Br J Cancer       Date:  2010-08-17       Impact factor: 7.640

3.  Avoidable deaths and random variation in patients' survival.

Authors:  K Seppä; T Hakulinen; E Läärä
Journal:  Br J Cancer       Date:  2012-04-24       Impact factor: 7.640

4.  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

5.  On standardized relative survival.

Authors:  Peter Sasieni; Adam R Brentnall
Journal:  Biometrics       Date:  2016-08-23       Impact factor: 2.571

6.  Temporal trends in relative survival following percutaneous coronary intervention.

Authors:  William J Hulme; Matthew Sperrin; Glen Philip Martin; Nick Curzen; Peter Ludman; Evangelos Kontopantelis; Mamas A Mamas
Journal:  BMJ Open       Date:  2019-02-19       Impact factor: 2.692

7.  Estimating the Loss in Expectation of Life and Relative Survival Rate among Hemodialysis Patients in Iran.

Authors:  Navisa Sadat Seyedghasemi; Abbas Bahrampour; Abbas Etminan; AliAkbar Haghdoost; Mohammad Reza Baneshi
Journal:  J Res Health Sci       Date:  2020-08-03

Review 8.  Cancer Patients' Survival: Standard Calculation Methods And Some Considerations Regarding Their Interpretation: POPULACIJSKO PREŽIVETJE BOLNIKOV Z RAKOM: UPORABA RAZLIČNIH PRISTOPOV IN PROBLEMI INTERPRETACIJE REZULTATOV.

Authors:  Vesna Zadnik; Tina Žagar; Maja Primic Žakelj
Journal:  Zdr Varst       Date:  2016-02-11
  8 in total

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