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Clustered restricted mean survival time regression.

Xinyuan Chen1, Michael O Harhay2,3, Fan Li4,5

  • 1Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS, USA.

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Summary

This study introduces a generalized restricted mean survival time (RMST) regression model for clustered data, offering a robust alternative to Cox regression for time-to-event analysis. Bias-corrected variance estimators improve accuracy in small samples, enhancing reliability in complex trial designs.

Keywords:
bias-corrected sandwich variance estimatorscluster randomized trialsgeneralized estimating equationsinverse probability of censoring weightsmultivariate survival analysistime-to-event outcomes

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Survival Analysis

Background:

  • The Cox regression model is standard for time-to-event outcomes in multicenter trials but assumes proportional hazards.
  • Violations of the proportional hazards assumption can lead to misinterpretation of hazard ratios.
  • Restricted mean survival time (RMST) is a model-free parameter recommended for survival analysis.

Purpose of the Study:

  • To generalize the RMST regression model for clustered data, accounting for within-cluster correlations.
  • To provide a robust method for analyzing time-to-event outcomes in complex study designs.
  • To develop bias-corrected variance estimators for improved inference in small-sample scenarios.

Main Methods:

  • Developed a generalized RMST regression model for clustered data.
  • Employed weighted generalized estimating equations for coefficient estimation.
  • Utilized cluster-robust sandwich variance estimators and proposed bias-corrected versions.
  • Evaluated methods through simulations and reanalysis of two multicenter randomized trials.

Main Results:

  • The proposed RMST regression model effectively handles clustered data and within-cluster correlations.
  • Standard sandwich variance estimators showed negative bias in small samples.
  • Bias-corrected sandwich variance estimators demonstrated improved performance in small-sample scenarios.
  • The generalized RMST model provides valid inference for time-to-event outcomes in complex trials.

Conclusions:

  • The generalized RMST regression model offers a valuable, model-free approach for analyzing time-to-event data in clustered settings.
  • Bias-corrected variance estimators are crucial for reliable inference when the number of clusters is limited.
  • This methodology enhances the interpretation and validity of results from multicenter and multilevel studies.