Bias in relative survival methods when using incorrect life‐tables: Lung and bladder cancer by smoking status and ethnicity in New Zealand

Abstract

Relative survival and excess mortality approaches are commonly used to estimate and compare net survival from cancer. These approaches are based on the assumption that the underlying (non-cancer) mortality rate of cancer patients is the same as that of the general population. This assumption is likely to be violated particularly in the context of smoking-related cancers. The magnitude of this bias has not been estimated. The objective of this article is to estimate the bias in relative survival ratios (RSRs) and excess mortality rate ratios (EMRRs) from using total population compared to correct subpopulation specific life-tables. Analyses were conducted on 1996-2001 linked census-cancer data (including smoking status) for people with lung and bladder cancer, using sex-specific (standard practice), sex-and ethnic-specific, sex-and smoking-specific and sex-, ethnic-and smoking-specific life-tables. Five-year RSRs using sex-specific life-tables, compared to fully stratified lifetables, were underestimated by 10-25% for current smoking and Maori populations. For example, the current smoker male bladder cancer RSR was 0.700 for sex-specific life-tables, compared to 0.838 for fully stratified life-tables. Similarly, EMRRs comparing current to never smokers and Maori to non-Maori were overestimated using sex-specific life-tables only: modestly only for lung cancer, but markedly for bladder cancer. For example, the EMRR comparing current to never smokers with bladder cancer in a fully adjusted regression model was 1.475 when using sex-specific life-tables only, but reduced to 1.098 when using fully stratified life-tables. Substantial bias can occur when estimating relative cancer survival across subpopulations if non-matching life-tables are used.Relative survival and excess mortality analyses are commonly used to estimate and compare net survival (or excess mortality) among patients with cancer. Relative survival and excess mortality analyses use overall survival and the total number of deaths, respectively, and then adjust for the expected survival and number of deaths using population life-tables. Relative survival ratios (RSRs) are calculated using the ratio of observed survival among cancer patients to the expected survival in the underlying population. Excess mortality rate modelling is a mirror image of survival analyses and usually undertaken with a Poisson model using the observed minus expected number of deaths as the dependent variable. 1-3The key advantages of relative survival and excess mortality rate methods are that error due to incorrect coding of cause of death is avoided, and that one captures cancerconsequent deaths through the difference in observed and expected deaths (or survival).2 The key disadvantage, however, is that one has to assume that the population lifetables provide accurate estimates of the expected mortality rate or survival for the people developing cancer. In other words, these methods assume that those who develop cancer would have had the same risk of mortality as the general pop...

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