Bayesian inference for Cox regression models using catalytic prior distributions
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View abstract on PubMed
Summary
This summary is machine-generated.We introduce the Cox catalytic prior for Bayesian inference in Cox models, improving stability for small sample sizes. This method enhances survival data analysis by offering a robust alternative to standard inference techniques.
Area Of Science
- Statistics
- Biostatistics
- Survival Analysis
Background
- The Cox proportional hazards model (Cox model) is widely used for survival data.
- Standard inference methods in Cox models face challenges with small sample sizes relative to model dimensions.
- Existing methods may not sufficiently stabilize complex parametric models in high-dimensional settings.
Purpose Of The Study
- To propose a novel Bayesian approach, the Cox catalytic prior, for enhancing Cox model inference.
- To address the limitations of standard maximum partial likelihood inference in small sample, high-dimensional scenarios.
- To provide a stable and consistent estimation method for Cox models.
Main Methods
- Formulation of the Cox catalytic prior using synthetic data and a surrogate baseline hazard.
- Generation of synthetic data from the predictive distribution of a simpler fitted model.
- Derivation of an approximate marginal posterior mode as a regularized log partial likelihood estimator.
Main Results
- The proposed Cox catalytic prior is proven to be proper under mild conditions.
- The resulting estimator demonstrates consistency.
- Simulation studies show superior performance compared to standard maximum partial likelihood inference and comparable results to existing shrinkage methods.
Conclusions
- The Cox catalytic prior offers a robust and effective Bayesian approach for Cox model inference, particularly in challenging small sample size scenarios.
- The method provides a stable and consistent estimator, outperforming traditional techniques.
- The approach is applicable to real-world survival data analysis.
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