Describing differences in survival curves

Menée à partir d'ensembles de données simulées, cette étude évalue l'intérêt d'une nouvelle mesure de l'effet d'un traitement dans un essai clinique, la probabilité nette d'une plus longue survie

Résumé en anglais

We congratulate Péron et al1 on their contribution to the medical canon of a new measure of survival time treatment effect in a clinical trial. This measure, which equals the chance of surviving longer in an experimental group vs a control group minus the reverse, is robust in that it depends on no distributional assumptions. It is easily interpretable and clinically relevant: if the measure is 100%, the experimental group is always better, if it is –100%, the experimental group is always worse, and if it is 0%, the 2 treatments are equivalent. The authors also describe a broader outcome in which survival is exceeded by a specified number of months. However, for simplicity we focus here on the situation in which the measure is 0%; that is, we are interested in superiority by any amount of time. In this case, we suggest for the sake of interpretability that we rescale to the simple chance of superiority by adding 100% and dividing by 2, yielding 100% and 0% for the extremes and 50% for equality of treatments.