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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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A class of semiparametric models for bivariate survival data
Walmir Dos Reis Miranda Filho1, Fábio Nogueira Demarqui2
1Statistics Department, Exact and Biological Sciences Institute, Federal University of Ouro Preto, Ouro Preto, Brazil. walmir.filho@ufop.edu.br.
Lifetime Data Analysis
|December 14, 2024
Summary
This study introduces flexible bivariate survival models using Archimedean copulas and the Yang-Prentice model. These models effectively analyze survival data, including crossing curves, offering improved statistical insights.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Bivariate survival models are crucial for analyzing time-to-event data with two related outcomes.
- Existing models may lack flexibility in handling complex dependencies and crossing survival curves.
Purpose of the Study:
- To propose a novel class of bivariate survival models.
- To enhance the analysis of survival data with improved flexibility and accuracy.
Main Methods:
- Utilizing Archimedean copulas (AMH, Clayton, Frank, GH, Joe) for dependency modeling.
- Employing the Yang-Prentice (YP) model for marginal distributions.
- Semiparametric baseline modeling with Piecewise Exponential (PE) and Bernstein Polynomials (BP).
- Inference via maximum likelihood (ML) estimation.
Main Results:
- The proposed models accommodate survival data with crossing curves.
- They generalize proportional hazards (PH) and proportional odds (PO) models.
- Semiparametric marginal modeling offers greater flexibility.
- Closed-form likelihood functions simplify inference.
Conclusions:
- The new bivariate survival models offer a flexible and robust framework for survival data analysis.
- Demonstrated versatility in analyzing ovarian cancer patient survival data.
- Provides a valuable tool for biostatisticians and researchers in related fields.
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