Restricted mean survival time regression model with time-dependent covariates

TL;DR

This paper introduces a new regression model for the restricted mean survival time (RMST) that incorporates time-dependent covariates, improving prediction accuracy in clinical follow-up studies.

Contribution

It develops a novel RMST regression model using IPCW for time-dependent covariates, validated through simulations and a heart transplantation case study.

Findings

The proposed model outperforms the time-dependent Cox model in prediction.

Simulation results confirm accurate estimation of regression parameters.

Application to heart transplantation data demonstrates practical utility.

Abstract

In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time-dependent covariates are becoming increasingly common in follow-up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time-dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time-dependent Cox model and the fixed (baseline) covariate RMST model, the time-dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions.