Reference-adjusted cancer survival measures. What are they, when are they useful, and how are they implemented in Stata?

Background: Ensuring fair comparisons of cancer survival statistics across population groups requires careful consideration of differential competing mortality due to other causes and adjusting for imbalances in terms of other prognostic covariates (for example, age). This has typically been achieved using comparisons of age-standardized net survival, with the age standardization addressing covariate imbalance and the net estimates removing differences in competing mortality from other causes. However, these estimates lack ease of interpretability. In this talk, I'll motivate an alternative approach that uses a common (reference) rate of other-cause mortality across groups to give reference-adjusted cancer survival measures. Methods: We'll discuss both the methodology and Stata implementation to enable both model-based and nonparametric estimation of reference-adjusted cancer survival metrics. These measures allow fair comparison of all-cause survival across groups with differential other-cause mortality (for exmaple, across countries, socioeconomic groups, or calendar periods). Results: These measures retain comparability but stay closer to the real-world risks of dying, allowing direct comparison across population groups with different covariate profiles and competing mortality patterns. In our illustrative example, we show regional variations in survival following a diagnosis of rectal cancer persist even after accounting for the regional variation in demographic profile of cancer patients and regional variation in other cause mortality. Conclusions: The methodological approach of using standardized and reference-adjusted metrics offers an appealing approach for future cancer survival comparison studies. The calculation of these metrics is readily available in Stata, building on the strong suite of official and community-contributed survival analysis commands.