A Bayesian Markov Framework for Modeling Breast Cancer Progression
This study develops a three-state Markov framework to estimate the transition rates between normal, preclinical screen-detectable phase (PCDP), and clinical breast cancer using simulated data. Two exponential models are explored: a five-mode transition model and a six-mode transition model, the latt...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/1/65 |
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| author | Tong Wu |
| author_facet | Tong Wu |
| author_sort | Tong Wu |
| collection | DOAJ |
| description | This study develops a three-state Markov framework to estimate the transition rates between normal, preclinical screen-detectable phase (PCDP), and clinical breast cancer using simulated data. Two exponential models are explored: a five-mode transition model and a six-mode transition model, the latter incorporating exact cancer case timings. Each model is analyzed both with and without covariates to evaluate their influence on breast cancer progression. Parameters are estimated utilizing maximum likelihood estimation and Bayesian models with Gibbs sampling to ensure robustness and methodological rigor. Additionally, a nonhomogeneous model based on the Weibull distribution is introduced to account for time-varying transition rates, providing a more dynamic perspective on disease progression. While the analysis is conducted with simulated data, the framework is adaptable to real-world datasets, offering valuable insights for refining screening policies and optimizing inter-screening intervals. |
| format | Article |
| id | doaj-art-e8b39fa0632b42f2acac6b24ef732beb |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-e8b39fa0632b42f2acac6b24ef732beb2025-01-10T13:18:08ZengMDPI AGMathematics2227-73902024-12-011316510.3390/math13010065A Bayesian Markov Framework for Modeling Breast Cancer ProgressionTong Wu0Department of Management Science and Information Systems, College of Management, University of Massachusetts Boston, Boston, MA 02125, USAThis study develops a three-state Markov framework to estimate the transition rates between normal, preclinical screen-detectable phase (PCDP), and clinical breast cancer using simulated data. Two exponential models are explored: a five-mode transition model and a six-mode transition model, the latter incorporating exact cancer case timings. Each model is analyzed both with and without covariates to evaluate their influence on breast cancer progression. Parameters are estimated utilizing maximum likelihood estimation and Bayesian models with Gibbs sampling to ensure robustness and methodological rigor. Additionally, a nonhomogeneous model based on the Weibull distribution is introduced to account for time-varying transition rates, providing a more dynamic perspective on disease progression. While the analysis is conducted with simulated data, the framework is adaptable to real-world datasets, offering valuable insights for refining screening policies and optimizing inter-screening intervals.https://www.mdpi.com/2227-7390/13/1/65breast cancerscreeningBayesianMarkov modelWinBUGS |
| spellingShingle | Tong Wu A Bayesian Markov Framework for Modeling Breast Cancer Progression Mathematics breast cancer screening Bayesian Markov model WinBUGS |
| title | A Bayesian Markov Framework for Modeling Breast Cancer Progression |
| title_full | A Bayesian Markov Framework for Modeling Breast Cancer Progression |
| title_fullStr | A Bayesian Markov Framework for Modeling Breast Cancer Progression |
| title_full_unstemmed | A Bayesian Markov Framework for Modeling Breast Cancer Progression |
| title_short | A Bayesian Markov Framework for Modeling Breast Cancer Progression |
| title_sort | bayesian markov framework for modeling breast cancer progression |
| topic | breast cancer screening Bayesian Markov model WinBUGS |
| url | https://www.mdpi.com/2227-7390/13/1/65 |
| work_keys_str_mv | AT tongwu abayesianmarkovframeworkformodelingbreastcancerprogression AT tongwu bayesianmarkovframeworkformodelingbreastcancerprogression |