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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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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
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.