• long-term survival;
  • non-proportional hazards;
  • time-dependent covariate effects


Randomized clinical trials with long-term survival data comparing two treatments often show Kaplan–Meier plots with crossing survival curves. Such behaviour implies a violation of the proportional hazards assumption for treatment. The Cox proportional hazards regression model with treatment as a fixed effect can therefore not be used to assess the influence of treatment of survival. In this paper we analyse long-term follow-up data from the Dutch Gastric Cancer Trial, a randomized study comparing limited (D1) lymph node dissection with extended (D2) lymph node dissection. We illustrate a number of ways of dealing with survival data that do not obey the proportional hazards assumption, each of which can be easily implemented in standard statistical packages. Copyright © 2005 John Wiley & Sons, Ltd.