Statistical issues in survival analysis (Win-loss composite time-to-event)
May 6, 2026 The win ratio introduced by Pocock et al (2012) has become a popular measure to summarize composite endpoints in clinical trials. It essentially prioritizes important events over lesser ones using an effect size as the relative frequency of wins (more favorable outcomes) to losses between treatment and comparator groups. They extended this into a proportional win-fractions regression model (PW) where the win ratio is the outcome modeled by covariates. To make treatment effects more interpretable, alternative metrics have been developed like net benefit, which is difference between win and loss proportions, and win odds, which is allocating half of the ties to each of the win and loss proportions which accounts for impact of the ties. In standard settings, a tie arises when both subjects are event-free by a certain point. As such, its probability is given by the product of two survival functions for the time to the first event (TFE), which can be predicted using any of the popular (univariate) survival models, such as the Cox proportional hazards (PH) model (Cox, 1972). They also consider the TFE along with the PW. In this paper, they follow this approach to predict the (time-dependent) win-loss probabilities based on the PW model, thereby enabling direct comparisons between subjects with pre-