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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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Bayesian framework for parametric bivariate accelerated lifetime modeling and its application to hospital acquired
D Bilgili1, D Ryu2, Ö Ergönül3
1Department of Mathematics and Statistics, University of North Florida, Jacksonville, Florida, U.S.A.
Biometrics
|September 23, 2015
Summary
This study introduces a Bayesian statistical model to analyze the relationship between colonization and infection times for Acinetobacter baumannii, a common hospital-acquired infection. The findings help understand disease progression and associated risk factors.
Area of Science:
- Statistics
- Epidemiology
- Infectious Diseases
Background:
- Infectious diseases pose a significant global health threat.
- Acinetobacter baumannii is a major cause of hospital-acquired infections.
- Statistical analysis of infectious disease data is crucial for public health.
Purpose of the Study:
- To develop a Bayesian methodology for analyzing the dependency between colonization and infection times.
- To investigate the association of covariates with these times.
- To apply a novel statistical model to real-world censored data.
Main Methods:
- Parametric bivariate accelerated lifetime model.
- Singular Bivariate Extreme Value distribution for residual modeling.
- Fully Bayesian framework for analysis of censored data.
Main Results:
- The proposed Bayesian methodology effectively models the dependency between colonization and infection times.
- Identified associations between covariates (age, gender, apache score, etc.) and disease progression.
- Demonstrated the application of the model to censored data from Turkish hospitals.
Conclusions:
- The developed Bayesian bivariate accelerated lifetime model provides a robust framework for analyzing infectious disease data.
- The methodology can be applied to understand colonization-infection dynamics and associated risk factors.
- This approach offers valuable insights for infection control strategies in healthcare settings.
Keywords:
A. baumanii bacteriaBayesian Survival AnalysisMarshall-Olkin distributionPosterior distributionSingular Bivariate Extreme Value distributionMore Related Videos
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