Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A...

In randomized controlled trials with survival time as the primary endpoint, it can be difficult to evaluate treatment effects using hazard ratios, particularly when the proportional hazards (PH) assumption is violated. The difference in restricted mean survival time (RMST) up to a pre-specified time point, $\tau$, is an alternative to the hazard ratio. However, the relative advantages of parametric (flexible parametric modeling [FPM]) and nonparametric approaches (direct integration of the Kaplan-Meier curve [DI], pseudo-observation [PO], and inverse probability of censoring weighting [IPCW]) for the estimation of RMST and its difference between groups are not clearly established. In this study, we performed comparative simulation studies to evaluate the performance of these methods for unadjusted and adjusted analyses in both PH and non-PH scenarios. For PO, IPCW, and FPM, we also considered a scenario where important covariates were adjusted via regression models. We obtained several key findings. For scenarios where the total number of events was $>35$, FPM tended to be unbiased, with higher power in PH scenarios. In non-PH scenarios, FPM exhibited slight bias with comparable power. For scenarios where the total number of events was $\le 35$, FPM showed unstable results. Among nonparametric methods, the bias and differences in power were negligible, except when the number of patients at risk at $\tau$ was $\le 15$; in this setting, IPCW showed conservative power and, under regression adjustment, a slight bias. These results provide a basis for the selection of methods to estimate RMST based on the expected prognosis.