Choice of relative or cause-specific approach to cancer survival analysis impacts estimates differentially by cancer type, population, and application: evidence from a Canadian population-based cohort study

Cause-specific (CS) and net survival in a relative survival framework (RS) are two of the most common methods for estimating cancer survival. In this paper, we assess the differences in results produced by two permutations of cause-specific and relative survival applied to estimating cancer survival and disparities in cancer survival, using data from First Nations and non-Aboriginal populations in Canada.

Subjects were members of the 1991 Canadian Census Mortality Cohort, a population-based cohort of adult respondents to the 1991 Long Form Census who have been followed up for incident cancers and death through linkage to administrative databases. We compared four methods: relative survival analyses with ethnicity-specific life tables (RS-ELT); relative survival with general population life tables (RS-GLT); cause-specific survival with a broad definition of cancer death (CS-Broad); and cause-specific survival with a narrow definition of cause of death (CS-Narrow) and applied these to the nine most common cancers among First Nations.

Apart from breast and prostate cancers, RS-ELT, RS-GLT, and CS-Broad tended to produce similar estimates of age-standardized five-year survival, whereas CS-Narrow yielded higher estimates of survival. CS-Narrow estimates were particularly unlike those based on the other methods for cancers of the digestive and respiratory tracts. Estimates of disparities in survival were generally comparable across the four methods except for breast and prostate cancers.

Cancer surveillance efforts in sub-populations defined by race, ethnicity, geography, socioeconomic status, or similar factors are necessary for identifying disparities and monitoring progress toward reducing them. In the absence of routine monitoring of cancer survival and cancer survival disparities in these populations, estimates generated by different methods will inevitably be compared over time and across populations. In this study, we demonstrate that caution should be exercised in making these comparisons, particularly in interpreting cause-specific survival rates with an unknown or narrow definition of cancer death and in estimates of breast and prostate cancer survival and/or disparities in survival generated by different methods.

Population health metrics. 2017 Jul 03*** epublish ***

Diana R Withrow, Jason D Pole, E Diane Nishri, Michael Tjepkema, Loraine D Marrett

Radiation Epidemiology Branch, National Cancer Institute, 9609 Medical Center Drive, Room 7E590, Bethesda, MD, 20892-9778, USA. ., Pediatric Oncology Group of Ontario, 480 University Avenue, Suite 1014, Toronto, ON, M5G 1V2, Canada., Population Health and Prevention, Prevention and Cancer Control, Cancer Care Ontario, 505 University Ave, 14th Floor, Toronto, ON, M5G 1X3, Canada., Health Analysis Division, Statistics Canada, 150 Tunney's Pasture Driveway, Ottawa, ON, K1A 0T6, Canada., Aboriginal Cancer Control Unit, Prevention and Cancer Control, Cancer Care Ontario, 505 University Ave, 14th Floor, Toronto, ON, M5G 1X3, Canada.