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

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...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2021-08, Vol.207, p.106155-106155, Article 106155
Main Authors: Yang, Zijing, Wu, Hongji, Hou, Yawen, Yuan, Hao, Chen, Zheng
Format: Article
Language:English
Subjects:
Conditional restricted mean survival time
Dynamic prediction
Nonproportional hazards
Survival analysis
Time-dependent covariates
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Summary: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 analyzed data from a study of primary biliary cirrhosis (PBC) to illustrate the use of the dynamic RMST model, and a simulation study was designed to test the advantages of the proposed approach. The predictive performance was evaluated using the C-index and the prediction error. Considering both the example results and the simulation results, 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 the optimization of evidence-based decision-making by updating personalized dynamic life expectancy for patients.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2021.106155