Restricted Mean Survival Time Approaches for Time-to-Event Outcomes in Prevention Research (MtG) 

The Cox proportional hazards model, proposed by Dr. Cox in 1972, has traditionally been employed in disease prevention studies with time-to-event outcomes as the primary endpoint. The hazard ratio (HR) is used to summarize the magnitude of the intervention effect in these studies. However, the limitations of using Cox’s HR as a summary of intervention effect magnitude have been widely discussed. Restricted Mean Survival Time (RMST), first introduced in 1949 by Dr. Irwin, has gained attention since approximately 2013 as an alternative to address the limitations of the traditional approach. There is now a growing body of literature demonstrating the applications of RMST in various prevention research contexts.
This presentation reviews the limitations of Cox’s HR and introduce the utilization of RMST as a robust alternative that calculates the mean survival time within a specified time window, offering a more intuitive interpretation of intervention effects. Dr. Uno then discusses several recent methodological advancements in RMST-based analysis. Furthermore, Dr. Uno introduces essential software tools for RMST analysis, including the survRM2 package in R, the RMSTREG procedure in SAS, and the strmst2 command in Stata. Use case examples are also provided to illustrate these concepts effectively.