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Related Experiment Videos

Fitting Weibull duration models with random effects

C Morris1, C Christiansen

  • 1Department of Statistics, Harvard University, Cambridge, MA 02138, USA. morris@stat.harvard.edu

Lifetime Data Analysis
|January 1, 1995
PubMed
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This study addresses correlated failure times in kidney transplant data using frailty models. The research combines parametric survival models with hierarchical Poisson regression for accurate analysis of patient outcomes across transplant centers.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Health Services Research

Background:

  • Correlated failure times are common in clustered data, necessitating advanced statistical models.
  • Kidney transplantation data exhibit patient-level outcomes clustered within transplant centers, leading to correlated graft failure times.
  • Traditional survival analyses may not adequately account for such hierarchical data structures.

Purpose of the Study:

  • To describe a method for fitting random effects hierarchical models (frailty models) to survival data with correlated failure times.
  • To illustrate the application of these models using kidney transplant graft failure data.
  • To demonstrate the combination of parametric survival regression and hierarchical Poisson regression software.

Main Methods:

Related Experiment Videos

  • Utilized frailty models to account for correlated failure times in clustered patient data.
  • Combined parametric survival regression software with PRIMM (Poisson Regression and Interactive Multilevel Modeling) for hierarchical Poisson regression.
  • Applied the methodology to a dataset of 412 patients across 11 kidney transplant centers.

    • Main Results:

    • Successfully fitted hierarchical models to kidney transplant data, accounting for center-specific effects.
    • Demonstrated the feasibility of combining different statistical software packages for complex survival analyses.
    • Provided an illustrative example of profiling data within a multilevel modeling framework.

    Conclusions:

    • Hierarchical frailty models are effective for analyzing correlated failure times in clustered survival data.
    • Combining specialized software packages enables robust analysis of complex health outcomes.
    • The described methodology offers a valuable approach for research involving patient outcomes across different healthcare facilities.