Estimating the 95% confidence interval for survival gain between an experimental anti-cancer treatment and a control

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). A growing number of new anticancer treatments are being approved by the European Medicines Agency (EMA) and/or the US Food and Drug Administration (FDA), and for this reason oncologists and decision makers are increasingly requested to interpret appropriately the results of survival studies. The mean survival (or mean lifetime survival) is in theory the best parameter to represent survival outcome in a given group of patients,1,2 but in most cases mean survival cannot be estimated from censored observations (unless the event rate reaches 100% in the patients concerned or unless the survival curve is extrapolated to infinity through complex mathematical functions such as those of Weibull3 or Gompertz4). In this framework, median survival is almost universally employed in clinical studies focused on time-to-event endpoints (irrespective of whether the endpoint is overall survival [OS] or progression-free survival [PFS]). Accordingly, median survival is considered an adequate proxy for mean survival.