Literature DB >> 22000329

Adjusting for the proportion of cancer deaths in the general population when using relative survival: a sensitivity analysis.

Sally R Hinchliffe1, Paul W Dickman, Paul C Lambert.   

Abstract

BACKGROUND: Relative survival is an extensively used method in population based cancer studies as it provides a measure of survival without the need for accurate cause of death information. It gives an estimate for the probability of dying from cancer in the absence of other causes by estimating the excess mortality in the study population when compared to an external group. The external group is usually the general population within a country or state and mortality estimates are taken from national life tables that are broken down by age, sex, calendar year and, where applicable, race/ethnicity. One potential bias when using relative survival that is most often overlooked occurs when there are a high proportion of deaths due to a specific cancer in the external group.
METHODS: This paper uses data from the Finnish Cancer Registry to illustrate, through the use of a simple sensitivity analysis, the impact that specific cancer deaths in the population mortality figures can have on the estimate of relative survival.
RESULTS: We found that when examining specific diseases such as breast cancer and colon cancer, the proportion of deaths due to these specific cancers in the general population is so small in comparison to the total mortality that they make little difference to the relative survival estimates. However, prostate cancer proved to be an exception to this. For all cancer sites combined the sensitivity analysis illustrates a major limitation for this type of analysis, particularly with the older age groups.
CONCLUSION: We recommend that, with a classification of diseases as wide as all cancer sites, relative survival should not be used without appropriate adjustment. Copyright Â
© 2011 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2011        PMID: 22000329     DOI: 10.1016/j.canep.2011.09.007

Source DB:  PubMed          Journal:  Cancer Epidemiol        ISSN: 1877-7821            Impact factor:   2.984


  10 in total

1.  The impact of state-specific life tables on relative survival.

Authors:  Antoinette M Stroup; Hyunsoon Cho; Steve M Scoppa; Hannah K Weir; Angela B Mariotto
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

2.  A competing risk analysis of death patterns in male genitourinary cancer.

Authors:  Atanu Bhattacharjee; Gaurav Roy; Atul Budukh; Rajesh Dikshit; Vijay M Patil; Amit Joshi; Vanita Noronha; Kumar Prabash; Papai Roy
Journal:  Cancer Rep (Hoboken)       Date:  2019-04-04

3.  Mixture Cure Models in Oncology: A Tutorial and Practical Guidance.

Authors:  Federico Felizzi; Noman Paracha; Johannes Pöhlmann; Joshua Ray
Journal:  Pharmacoecon Open       Date:  2021-02-26

4.  Should relative survival be used with lung cancer data?

Authors:  S R Hinchliffe; M J Rutherford; M J Crowther; C P Nelson; P C Lambert
Journal:  Br J Cancer       Date:  2012-05-03       Impact factor: 7.640

Review 5.  Critical Points for Interpreting Patients' Survival Rate Using Cancer Registries: A Literature Review.

Authors:  Ayako Okuyama; Akiko Shibata; Hiroshi Nishimoto
Journal:  J Epidemiol       Date:  2017-10-28       Impact factor: 3.211

6.  Reporting net survival in populations: a sensitivity analysis in lung cancer demonstrates the differential implications of reporting relative survival and cause-specific survival.

Authors:  Kay See Tan; Takashi Eguchi; Prasad S Adusumilli
Journal:  Clin Epidemiol       Date:  2019-09-02       Impact factor: 4.790

7.  Direct modeling of the crude probability of cancer death and the number of life years lost due to cancer without the need of cause of death: a pseudo-observation approach in the relative survival setting.

Authors:  Dimitra-Kleio Kipourou; Maja Pohar Perme; Bernard Rachet; Aurelien Belot
Journal:  Biostatistics       Date:  2022-01-13       Impact factor: 5.899

8.  Geographical, racial and socio-economic variation in life expectancy in the US and their impact on cancer relative survival.

Authors:  Angela B Mariotto; Zhaohui Zou; Christopher J Johnson; Steve Scoppa; Hannah K Weir; Bin Huang
Journal:  PLoS One       Date:  2018-07-25       Impact factor: 3.240

9.  Marginal measures and causal effects using the relative survival framework.

Authors:  Elisavet Syriopoulou; Mark J Rutherford; Paul C Lambert
Journal:  Int J Epidemiol       Date:  2020-04-01       Impact factor: 7.196

10.  The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study.

Authors:  Camille Maringe; James Spicer; Melanie Morris; Arnie Purushotham; Ellen Nolte; Richard Sullivan; Bernard Rachet; Ajay Aggarwal
Journal:  Lancet Oncol       Date:  2020-07-20       Impact factor: 54.433

  10 in total

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