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A Bayesian survival treed hazards model using latent Gaussian processes.

Richard D Payne1, Nilabja Guha2, Bani K Mallick3

  • 1Eli Lilly & Company, Lilly Corporate Center, Indianapolis, IN, 46285, United States.

Biometrics
|February 16, 2024
PubMed
Summary

We introduce a flexible Bayesian model for time-to-event data analysis. This new approach offers clear inference and can identify patient subgroups and biomarkers, outperforming existing methods.

Keywords:
hazards modelslaplace approximationreversible jump MCMCsurvival analysistime-to-event datatree partitions

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Machine Learning

Background:

  • Survival models are crucial for analyzing time-to-event data across various fields.
  • Traditional proportional hazard models offer interpretability but may violate assumptions.
  • Non-parametric models provide flexibility but often lack a robust inferential framework.

Purpose of the Study:

  • To propose a novel Bayesian treed hazards partition model that combines flexibility with a clear inferential framework for time-to-event data.
  • To develop a method capable of identifying patient subgroups and prognostic/predictive biomarkers.
  • To address limitations of existing survival analysis techniques.

Main Methods:

  • A Bayesian treed hazards partition model is proposed, utilizing a latent Gaussian process to model the log-hazard function within partitions.
  • An efficient reversible jump Markov chain Monte Carlo algorithm is employed, achieved by marginalizing partition parameters via Laplace approximation.
  • Consistency properties of the proposed estimator are theoretically established.

Main Results:

  • The proposed model demonstrates both flexibility and inferential capability, overcoming limitations of existing methods.
  • The method successfully identified subgroups and biomarkers in simulated data and a real-world liver cirrhosis dataset.
  • Performance was evaluated against established survival analysis techniques.

Conclusions:

  • The Bayesian treed hazards partition model offers a powerful and flexible approach to survival data analysis.
  • This method facilitates the discovery of patient subgroups and predictive biomarkers, enhancing clinical and research applications.
  • The developed algorithm provides an efficient and statistically sound framework for complex survival data.