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The multivariate Bernoulli detector: change point estimation in discrete survival analysis.

Willem van den Boom1, Maria De Iorio1,2, Fang Qian1

  • 1Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.

Biometrics
|August 13, 2024
PubMed
Summary

This study introduces a new method for analyzing competing risks in discrete time-to-event data. The multivariate Bernoulli detector improves estimation accuracy for cause-specific hazards and risk dependence.

Keywords:
Bayesian statisticscompeting risksdiscrete failure time modelsdiscrete time-to-event datagrouped survival datalocal-global Markov chain Monte Carlo

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Machine Learning

Background:

  • Time-to-event data analysis often involves discrete scales and multiple competing risks.
  • Standard continuous survival analysis methods yield biased estimates when applied to such data.
  • Accurate modeling of competing risks is crucial in medical research and healthcare analytics.

Purpose of the Study:

  • To propose a novel statistical model, the multivariate Bernoulli detector, for analyzing discrete time-to-event data with competing risks.
  • To address the limitations of existing methods that suffer from biased estimation.
  • To enable data-driven learning of change point numbers and their dependence across risks.

Main Methods:

  • Development of a multivariate change point model for cause-specific baseline hazards.
  • Incorporation of priors on the number and location of change points to model dependence across risks.
  • Utilizing a multivariate Bernoulli prior for inferring involved risks conditionally on change points.
  • Implementation of a tailored local-global Markov chain Monte Carlo (MCMC) algorithm for full posterior inference.

Main Results:

  • The proposed model effectively handles competing risks in discrete time-to-event data.
  • Accurate estimation of cause-specific hazard rates and dependence across risks was achieved.
  • Demonstrated superior performance compared to existing approaches in simulations and ICU data analysis.

Conclusions:

  • The multivariate Bernoulli detector offers a robust and accurate framework for discrete time-to-event data with competing risks.
  • The method provides valuable insights into the dependence structure between different risks over time.
  • This approach enhances the reliability of survival analysis in complex clinical and research settings.