Pseudo-death: a new measure to evaluate composite outcomes in survival analysis

Abstract Traditional methods for analysing composite survival endpoints, such as the proportional hazards model, proportional mean, proportional win-fraction models (win ratio), and multi-state models, are limited by the subjective nature of event ranking, or by their inability to integrate multiple events into a unified measure. We propose a statistical method that transforms clinical observations into ‘pseudo-death’ by rescaling nonfatal events on the scale of death (considering death as the gold standard for survival analysis), generating scaling factors representing the conditional probability of death given the observed data. We then utilize pseudo-death to conduct the Z test and permutation test for evaluating treatment efficacy at a single time point, and the Wald test for multiple time points. We apply pseudo-death analysis to data from the V325 trial, which evaluates the addition of docetaxel to a cisplatin and fluorouracil regimen compared to cisplatin and fluorouracil alone in advanced gastric cancer. The pseudo-death framework detects significant treatment effects missed by traditional methods. In addition, simulation studies indicate that the proposed pseudo-death approach outperforms existing composite endpoint methods in various scenarios. Due to its simplicity, interpretability, and efficiency, the pseudo-death approach serves as a powerful tool for analysing composite endpoints.