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

Authors Tan KS, Eguchi T, Adusumilli PS

Received 2 April 2019

Accepted for publication 25 July 2019

Published 2 September 2019 Volume 2019:11 Pages 781—792

DOI https://doi.org/10.2147/CLEP.S210894

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Professor Henrik Toft Sørensen


Kay See Tan,1 Takashi Eguchi,2 Prasad S Adusumilli2

1Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017, USA; 2Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA

Correspondence: Kay See Tan
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, New York, NY 10017, USA
Tel +1 646 888 8257
Email tank@mskcc.org

Background: Net survival is commonly quantified as relative survival (observed survival among lung cancer patients versus expected survival among the general population) and cause-specific survival (lung cancer–specific survival among lung cancer patients). These approaches have drastically different assumptions; hence, failure to distinguish between them results in significant implications for study findings. We quantified the differences between relative and cause-specific survival when reporting net survival of patients with non-small cell lung cancer (NSCLC).
Methods: Cases of NSCLC diagnosed between 2004 and 2014 were extracted from the Surveillance, Epidemiology, and End Results database. The net survival of each stage-by-age stratum was expressed as cause-specific survival (Kaplan-Meier approach) and relative survival (Ederer II approach); percentage-point (pp) differences between the survival estimates were quantified up to 10 years postdiagnosis.
Results: Analyses included 263,894 cases. Cause-specific survival estimates were higher than relative survival estimates across all strata. Although the differences were negligible at 1 year postdiagnosis, they increased with increasing years of follow-up, up to 9.3 pp at 10 years (eg, aged 60–74 with stage I disease: 53.0% vs 43.7%). Differences in survival estimates between the methods also increased by increasing age groups (eg, at 10 years postdiagnosis: 5.1 pp for ages 18–44, 8.8 pp for ages 45–59, and 9.3 pp for ages 60–74) but decreased drastically for those aged ≥75 (3.1 pp).
Conclusion: Relative survival and cause-specific survival are not interchangeable. The type of survival estimate used in cancer studies should be specified, particularly for long-term survival.

Keywords: biostatistics, cancer epidemiology, epidemiological methods, mortality, lung cancer

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