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

TL;DR

This paper introduces a dynamic RMST model based on conditional RMST that incorporates time-dependent covariates, improving the prediction of patient survival over time in clinical settings with nonproportional hazards.

Contribution

It develops a novel dynamic RMST model using cRMST and demonstrates its superior predictive performance over traditional RMST models in survival analysis.

Findings

Dynamic RMST model effectively captures changing prognostic effects.

Model shows improved predictive accuracy with higher C-index.

Illustrated with primary biliary cirrhosis patient data.

Abstract

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