Interpreting Net Survival: What We Estimate Versus What We Think We Estimate

TL;DR

This paper critically examines the interpretation of net survival in cancer epidemiology, revealing that current estimators conflate cancer-specific mortality with other causes, especially when excess mortality sources are present, leading to potential underestimation.

Contribution

It clarifies the limitations of the Pohar Perme estimator in the presence of excess mortality sources and advocates for more precise language and interpretation of net survival.

Findings

Pohar Perme estimator sums cancer, baseline, and treatment-induced deaths.

Relative risk of other-cause death varies widely across cancers.

Net survival can underestimate true cancer-specific survival when excess mortality exists.

Abstract

Net survival is conventionally defined as ``survival if cancer were the only possible cause of death'', an estimand corresponding to cancer-specific mortality alone. The Pohar Perme estimator targets this by removing general population other-cause mortality from observed total mortality, but achieves it only when cancer patients experience the same other-cause mortality as the general population. However, cancer patients often experience elevated other-cause mortality due to baseline health differences and treatment-induced effects. Using recent theoretical work decomposing total mortality into four components (cancer deaths, baseline health differences, treatment-induced other-cause deaths, and general population other-cause mortality), we show that the Pohar Perme estimator delivers the sum of cancer deaths, baseline differences, and treatment-induced deaths, falling short of its intended estimand whenever either source of excess is present. From Botta \textit{et al}, we present empirical evidence showing relative risk of other-cause deaths ranging from 1.0 (colorectal cancer) to 4.0+ (head and neck cancers), and calculations demonstrating that net survival can substantially underestimate cancer-specific survival probability when relative risk exceeds 1.0. Critically, treatment-induced other-cause deaths represent irreducible causal pathways from cancer to death that cannot be eliminated through better stratification. We recommend interpreting net survival as ``survival where general population other-cause mortality is removed'' rather than as a causal counterfactual, and call for more precise language in cancer epidemiology.