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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Multilevel model with random effects for clustered survival data with multiple failure outcomes.

Richard Tawiah1, Kelvin K W Yau2, Geoffrey J McLachlan3

  • 1School of Medicine and Menzies Health Institute Queensland, Griffith University, Queensland, Australia.

Statistics in Medicine
|November 27, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a multilevel frailty model for complex survival data, effectively handling patient heterogeneity and repeated events in clinical trials. The model improves analysis of multi-institutional studies, offering better insights into treatment effects.

Keywords:
GLMMmultilevel frailty modelrandom baseline riskrandom treatment-by-institution interactionrecurrent event dataserial dependence

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Survival Analysis

Background:

  • Survival data in multi-institutional trials often exhibit clustering and multiple failure outcomes.
  • Existing models struggle to simultaneously address serial dependence and heterogeneity in such complex data structures.
  • Recurrent events, like respiratory exacerbations in cystic fibrosis, require specialized analytical approaches.

Purpose of the Study:

  • To develop and present a novel multilevel frailty model for survival data with multilevel structures.
  • To address serial dependence and simultaneous heterogeneity in multi-institutional randomized controlled trials.
  • To extend the proportional hazards model to incorporate random effects for institution and patient-level dependencies.

Main Methods:

  • Proposed a multilevel frailty model extending the proportional hazards model.
  • Incorporated random covariates and institution effects to capture interactions and baseline risk variations.
  • Utilized generalized linear mixed model methodology, assuming normal random effects distribution, with residual maximum likelihood and maximum likelihood for parameter estimation.

Main Results:

  • Simulation studies evaluated the performance of estimation methods and the impact of misspecified random effects.
  • The model demonstrated practical feasibility through analysis of real data from a cystic fibrosis clinical trial.
  • The model successfully accounted for treatment-by-institution interactions and institutional variations in risk.

Conclusions:

  • The proposed multilevel frailty model is effective for analyzing complex survival data from multi-institutional trials.
  • The methodology provides a robust framework for handling recurrent events, serial dependence, and heterogeneity.
  • This approach enhances the analysis of clinical trial data, particularly for diseases with repeated outcomes.