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A Flexible Bayesian Parametric Proportional Hazard Model: Simulation and Applications to Right-Censored Healthcare
Abdisalam Hassan Muse1, Oscar Ngesa2, Samuel Mwalili3
1Department of Mathematics (Statistics Option), Pan African University Institute for Basic Science Technology and Innovation (PAUSTI), Nairobi 62000 00200, Kenya.
Journal of Healthcare Engineering
|June 13, 2022
Summary
This study introduces a Bayesian approach for survival analysis using a generalized log-logistic proportional hazard model. The method effectively analyzes healthcare survival data, offering a flexible alternative to classical inference.
Area of Science:
- Biomedical Sciences
- Statistical Modeling
- Computational Statistics
Background:
- Survival analysis is crucial in biomedical and scientific research for event time analysis.
- Advancements in computation have spurred the adoption of Bayesian techniques as a flexible alternative to classical inference.
Purpose of the Study:
- To apply Bayesian inference to a generalized log-logistic proportional hazard model for right-censored healthcare data.
- To estimate model parameters using Markov chain Monte Carlo (McMC) simulation.
Main Methods:
- Utilized independent gamma priors for baseline hazard parameters and normal priors for regression coefficients.
- Employed Markov chain Monte Carlo (McMC) via Gibbs sampling in Bayesian analysis using Gibbs sampling (BUGS) syntax within R (JAGS).
- Assessed model performance through a detailed simulation study and analysis of two real-world healthcare survival datasets.
Main Results:
- The joint posterior distribution of regression coefficients and parameters was derived.
- The proposed parametric proportional hazard model demonstrated strong performance in simulations and real data analysis.
- Convergence diagnostic tests confirmed the reliability of the Bayesian estimation method.
Conclusions:
- The developed Bayesian parametric proportional hazard model is effective for analyzing various survival data types.
- This approach offers a valuable tool for healthcare survival data analysis, complementing classical methods.
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