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Random effect restricted mean survival time model.

Keisuke Hanada1, Masahiro Kojima2

  • 1Department of Biostatistics, Faculty of Medicine, Wakayama Medical University.

Journal of Biopharmaceutical Statistics
|April 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We introduce a novel random-effects restricted mean survival time (RMST) model to address cluster-level variability. This new approach enhances survival analysis by incorporating random effects, offering a more comprehensive understanding of time-to-event data in clustered settings.

Keywords:
Restricted mean survival timecluster effectinverse probability censoring weightpseudo-valuerandom effect

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Restricted Mean Survival Time (RMST) is a clinically intuitive survival measure.
  • Existing RMST models can adjust for covariates but lack methods for cluster-level variability.
  • There is a need for statistical models that account for random effects in RMST analysis.

Purpose of the Study:

  • To propose a novel random-effects restricted mean survival time (RMST) model.
  • To develop analytical methods for incorporating random effects into RMST analysis.
  • To evaluate the performance and applicability of the proposed methods.

Main Methods:

  • A generalized mixed model utilizing pseudo-values.
  • An approach integrating inverse probability censoring weighting (IPCW) estimating equations within clusters.
  • Computer simulations to assess method performance.
  • Application to real-world data on maternal age and under-five mortality in India.

    • Main Results:

    • The proposed random-effects RMST model effectively accounts for cluster-level variability.
    • Both analytical methods demonstrated reliable performance in simulations.
    • The model successfully analyzed the impact of maternal age on under-five mortality in India.

    Conclusions:

    • The developed random-effects RMST model provides a robust framework for analyzing clustered time-to-event data.
    • The proposed methods offer valuable tools for biostatisticians and epidemiologists.
    • This approach enhances the understanding of survival outcomes in populations with inherent clustering.