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Updated: May 31, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Bayesian methods for fitting mixture models that characterize branching tree processes: An application to development
Alane Izu1, Ted Cohen, Carole Mitnick
1Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. aizu@hsph.harvard.edu
Statistics in Medicine
|July 1, 2011
Summary
Understanding the sequence of antibiotic resistance mutations is key for treating drug-resistant tuberculosis (TB). This study introduces a Bayesian branching tree model to infer mutation order from patient samples, aiding treatment policy development.
Area of Science:
- * Infectious Diseases
- * Computational Biology
- * Microbiology
Background:
- * Pathogen antibiotic resistance is a growing global health concern, necessitating effective treatment strategies.
- * Understanding the genetic mutation order in antibiotic resistance development is crucial for optimizing combination therapy and preventing treatment failure.
- * Cross-sectional patient data, while common, presents challenges in reconstructing the temporal sequence of resistance mutations.
Purpose of the Study:
- * To develop and apply a novel Bayesian approach for fitting branching tree models to infer the order of drug-resistance mutations.
- * To leverage existing statistical methods for analyzing cross-sectional data to determine mutation sequences.
- * To apply the proposed model to real-world data for drug-resistant tuberculosis (TB) in Peru to inform treatment policies.
Main Methods:
- * Development of a Bayesian framework for fitting branching tree models, enhancing previous statistical approaches.
- * Incorporation of prior information on measurement error and cross-resistance to improve model accuracy.
- * Application of the model to a dataset of drug-resistant TB cases in Peru, analyzing the order of resistance mutations.
Main Results:
- * The Bayesian branching tree model successfully inferred the order of drug-resistance mutations from cross-sectional patient data.
- * The model demonstrated advantages in accommodating prior knowledge and quantifying uncertainty in mutation ordering.
- * Analysis of the Peruvian TB dataset provided insights into the sequence of resistance development to common TB drugs.
Conclusions:
- * The proposed Bayesian branching tree model offers a robust method for determining the order of genetic mutations conferring antibiotic resistance.
- * This approach can significantly aid in the development of effective treatment policies for drug-resistant pathogens, such as TB.
- * The findings highlight the importance of considering mutation order in clinical decision-making for infectious diseases.
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