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

• Published in: British journal of cancer Vol. 129; no. 5; pp. 819 - 828
• Main Authors: Myklebust, Tor Åge, Aagnes, Bjarte, Nilssen, Yngvar, Rutherford, Mark, Lambert, Paul C., Andersson, Therese M. L., Johansson, Anna L. V., Dickman, Paul W., Møller, Bjørn
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.09.2023; Nature Publishing Group
• Subjects: 692/4028/67/2324; 692/700/1750; Algorithms; Automation; Biomedical and Life Sciences; Biomedicine; Cancer; Cancer Research; Drug Resistance; Epidemiology; Estimates; Feasibility Studies; Humans; Life span; Medical prognosis; Molecular Medicine; Neoplasms - therapy; Oncology; Probability; Statistics; Survival; Survival Analysis
• ISSN: 0007-0920; 1532-1827; 1532-1827
• DOI: 10.1038/s41416-023-02360-5

Background Routine 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. Methods We 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. Results For 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. Discussion It 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.