Summarizing and communicating on survival data according to the audience: a tutorial on different measures illustrated with population-based cancer registry data

• Published in: Clinical epidemiology 2019-01, Vol.11, p.53-65
• Main Authors: Belot, Aurélien, Ndiaye, Aminata, Luque-Fernandez, Miguel-Angel, Kipourou, Dimitra-Kleio, Maringe, Camille, Rubio, Francisco Javier, Rachet, Bernard
• Format: Article
• Language: English
• Subjects: Analysis; Biometrics; Cancer; Clinical medicine; Colon cancer; Competing risks; Conditional survival; Crude Probability of Death; Disease; Epidemiology; Information management; Lifetime; Medical prognosis; Methodology; Mortality; Net survival; Number of Life Years Lost; Patients; Population; Probability; Public health; Relative survival setting; Restricted Mean Survival Time; Socioeconomic factors; Stroke; Studies; Survival
• ISSN: 1179-1349; 1179-1349
• DOI: 10.2147/CLEP.S173523

Survival data analysis results are usually communicated through the overall survival probability. Alternative measures provide additional insights and may help in communicating the results to a wider audience. We describe these alternative measures in two data settings, the overall survival setting and the relative survival setting, the latter corresponding to the particular competing risk setting in which the cause of death is unavailable or unreliable. In the overall survival setting, we describe the overall survival probability, the conditional survival probability and the restricted mean survival time (restricted to a prespecified time window). In the relative survival setting, we describe the net survival probability, the conditional net survival probability, the restricted mean net survival time, the crude probability of death due to each cause and the number of life years lost due to each cause over a prespecified time window. These measures describe survival data either on a probability scale or on a timescale. The clinical or population health purpose of each measure is detailed, and their advantages and drawbacks are discussed. We then illustrate their use analyzing England population-based registry data of men 15-80 years old diagnosed with colon cancer in 2001-2003, aiming to describe the deprivation disparities in survival. We believe that both the provision of a detailed example of the interpretation of each measure and the software implementation will help in generalizing their use.