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In the process of clinical diagnosis and treatment, the restricted meansurvival time (RMST), which reflects the life expectancy of patients up to aspecified time, can be used as an appropriate outcome measure. However, theRMST only calculates the mean survival time of patients within a period of timeafter the start of follow-up and may not accurately portray the change in apatient's life expectancy over time. The life expectancy can be adjusted forthe time the patient has already survived and defined as the conditionalrestricted mean survival time (cRMST). A dynamic RMST model based on the cRMSTcan be established by incorporating time-dependent covariates and covariateswith time-varying effects. We analysed data from a study of primary biliarycirrhosis (PBC) to illustrate the use of the dynamic RMST model. The predictiveperformance was evaluated using the C-index and the prediction error. Theproposed dynamic RMST model, which can explore the dynamic effects ofprognostic factors on survival time, has better predictive performance than theRMST model. Three PBC patient examples were used to illustrate how thepredicted cRMST changed at different prediction times during follow-up. The useof the dynamic RMST model based on the cRMST allows for optimization ofevidence-based decision-making by updating personalized dynamic life expectancyfor patients.

Dynamic prediction and analysis based on restricted mean survival time in survival analysis with nonproportional hazards