• Aalen model;
  • causal effect;
  • confounding;
  • odds of concordance;
  • survival data


A simple summary of a treatment effect is attractive, which is part of the explanation of the success of the Cox model when analysing time-to-event data since the relative risk measure is such a convenient summary measure. In practice, however, the Cox model may fail to give a reasonable fit, very often because of time-changing treatment effect. The Aalen additive hazards model may be a good alternative as time-changing effects are easily modelled within this model, but results are then evidently more complicated to communicate. In such situations, the odds of concordance measure (OC) is a convenient way of communicating results, and recently Martinussen & Pipper (2012) showed how a variant of the OC measure may be estimated based on the Aalen additive hazards model. In this study, we propose an estimator that should be preferred in observational studies as it always estimates the causal effect on the chosen scale, only assuming that there are no un-measured confounders. The resulting estimator is shown to be consistent and asymptotically normal, and an estimator of its limiting variance is provided. Two real applications are provided.