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Bayesian bivariate cure rate models using Gaussian copulas.

Seoyoon Cho1, Matthew A Psioda2, Joseph G Ibrahim3

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

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Summary
This summary is machine-generated.

This study introduces a new cure rate model for multiple time-to-event outcomes, addressing limitations in traditional survival analysis for diseases like melanoma. The novel approach accurately models dependent events in populations with a cured segment.

Keywords:
Bivariate survival modelCure rate modelMelanoma clinical trialTruncated Gaussian copula

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Statistics

Background:

  • Traditional survival models assume all individuals are at risk for an event.
  • Cure rate models are necessary when a population subset is not susceptible to an event, such as in melanoma treatment.
  • Joint modeling of multiple time-to-event outcomes is crucial in oncology.

Purpose of the Study:

  • To propose a novel joint model for multiple time-to-event outcomes incorporating a cure structure.
  • To address the limitations of traditional survival models in scenarios with a cured population.
  • To provide a method for analyzing dependent time-to-event outcomes in the presence of cure.

Main Methods:

  • Development of a joint model using a novel truncated Gaussian copula for bivariate time-to-event outcomes.
  • Formulation of the joint model directly on time-to-event outcomes, not conditional on cure status.
  • Modeling dependency between outcomes via the copula's correlation matrix.
  • Utilizing a Markov Chain Monte Carlo procedure for model fitting.

Main Results:

  • The proposed model effectively handles multiple time-to-event outcomes with a cure structure.
  • Simulation studies demonstrated the method's performance.
  • A real-data analysis using melanoma clinical trial data validated the model's utility.
  • Comparison with independent models highlighted the benefits of the joint approach.

Conclusions:

  • The novel truncated Gaussian copula-based joint model offers a valuable tool for analyzing time-to-event data with cure fractions.
  • This multivariate approach is particularly relevant for oncology studies with multiple endpoints.
  • The method provides a more accurate dependency structure than independent models when cure is present.