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A new cure rate regression framework for bivariate data based on the Chen distribution.

Ricardo Puziol de Oliveira1, Marcos Vinicius de Oliveira Peres1, Edson Z Martinez1

  • 1Ribeirão Preto Medical School, 54539University of São Paulo, Ribeirão Preto, SP, Brazil.

Statistical Methods in Medical Research
|September 21, 2022
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Summary

This study presents a novel mixture cure rate model using the Chen distribution for recurrent event data. The model offers an alternative to traditional methods for analyzing complex survival data with cure fractions.

Keywords:
Bayesian approachChen distributioncure rate modelsurvival regression analysis

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Recurrent event data analysis often requires accounting for a 'cure fraction' where some individuals do not experience the event.
  • Existing models, like the Cox proportional hazards model, may not fully capture complex survival data structures, especially with mixture components.

Purpose of the Study:

  • To introduce a new multivariate mixture cure rate model based on the Chen probability distribution for recurrent event data.
  • To propose a bivariate parametric model for analyzing bivariate lifetime data with covariates, censoring, and a cure fraction.
  • To provide a Bayesian framework for analyzing medical datasets with these advanced statistical models.

Main Methods:

  • Development of a multivariate mixture cure rate model utilizing the Chen distribution.
  • Formulation of a bivariate parametric mixture model for handling complex lifetime data.
  • Application of a Bayesian approach for model estimation and analysis.
  • Utilizing Cox-Snell residuals for model validation.

Main Results:

  • The proposed Chen-based mixture cure rate model effectively analyzes recurrent event data with a cure fraction.
  • The bivariate parametric model demonstrated suitability for analyzing complex lifetime data with mixture structures.
  • Model validation using Cox-Snell residuals confirmed the appropriateness of the new mixture cure rate models.

Conclusions:

  • The novel Chen distribution-based mixture cure rate model provides a robust alternative for analyzing recurrent event data with cure fractions.
  • The developed bivariate parametric model and Bayesian framework are effective for complex survival data analysis in medical research.
  • The study successfully validates the proposed models for real-world applications in diseases like leukemia and diabetic retinopathy.