Improving communication of cancer survival statistics-feasibility of implementing model-based algorithms in routine publications

BackgroundRoutine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics.MethodsWe used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients.ResultsFor 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites.DiscussionIt may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients.

Automated summary

Background: Routine reporting of cancer patient survival is vital for monitoring healthcare effectiveness and prognosis. Objective: This study aims to explore the feasibility of automating the production of these statistics. Results: We obtained reliable estimates of survival rates for most cancer types. Discussion: Implementing new survival measures in routine publications may be challenging, but we propose a method for automating the production of these statistics.