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PICBayes: Bayesian proportional hazards models for partly interval-censored data
Communications in Statistics: Simulation and Computation
|March 21, 2025
Summary
This study introduces PICBayes, an R package for analyzing partly interval-censored data using Bayesian proportional hazards models. It offers flexible tools for various complex survival data scenarios in medical research.
Area of Science:
- Biostatistics
- Survival Analysis
- Computational Statistics
Background:
- Partly interval-censored (PIC) data are common in medical studies, especially for tracking disease progression like progression-free survival.
- The proportional hazards (PH) model is a standard tool in survival analysis, but handling PIC data requires specialized methods.
- Existing methods may not adequately address the complexities found in real-world medical data, necessitating advanced statistical approaches.
Purpose of the Study:
- To introduce the PICBayes R package for fitting proportional hazards models to partly interval-censored data.
- To provide functions for various complexities of PIC data, including spatial and clustered data structures.
- To offer a Bayesian semiparametric framework for robust survival data analysis.
Main Methods:
- Development of the PICBayes R package implementing Bayesian semiparametric proportional hazards models.
- Inclusion of functions for standard PIC data, spatially referenced data with frailty, and clustered data with random effects.
- Support for normal and Dirichlet process mixture priors for random effects, allowing flexible modeling.
- Adaptation of functions for general interval-censored data.
Main Results:
- The PICBayes package provides a comprehensive suite of tools for analyzing diverse types of partly interval-censored survival data.
- Demonstrated flexibility in handling spatial dependencies and hierarchical data structures within the PH model framework.
- Successful application of the package to two real-world medical datasets, validating its practical utility.
Conclusions:
- The PICBayes R package offers a powerful and flexible Bayesian approach for analyzing complex partly interval-censored survival data.
- It enhances the ability of researchers to model disease progression and survival outcomes more accurately in medical studies.
- The package facilitates the application of advanced statistical methodologies to challenging real-world biomedical data.
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