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05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
A Bayesian model for censored positive count data in evaluating breast cancer progression
Hung-Wen Yeh1, Yu Jiang, Lili Garrard
1Department of Biostatistics, The University of Kansas Medical Center, Kansas City, Kansas 66160 ; The University of Kansas Cancer Center, Kansas City, Kansas 66160.
Model Assisted Statistics and Applications : an International Journal
|September 3, 2013
Summary
Researchers studied ductal carcinoma in situ (DCIS) progression using animal models and statistical analysis. They developed zero-truncated Poisson models to analyze censored data, finding simpler models favored for DCIS subtypes.
Area of Science:
- Oncology
- Biostatistics
- Translational Research
Background:
- Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer.
- Animal models are used to study human cancer progression.
- Statistical analysis of censored data is crucial in cancer research.
Purpose of the Study:
- To analyze censored data from DCIS progression in animal models.
- To apply zero-truncated Poisson (ZTP) models with informative priors.
- To compare models with group-specific versus homogeneous parameters for DCIS subtypes.
Main Methods:
- Transplantation of human DCIS tissues into animal models.
- Fitting zero-truncated Poisson models with gamma priors.
- Utilizing Markov chain Monte Carlo and grid approximation for posterior distributions.
- Model comparison using Deviance Information Criterion (DIC).
Main Results:
- Zero-truncated Poisson models were successfully fitted to censored DCIS data.
- Bayes estimates were comparable to maximum likelihood estimates.
- The Deviance Information Criterion favored simpler, homogeneous models for both cell lines.
Conclusions:
- Statistical modeling provides insights into DCIS progression.
- Zero-truncated Poisson models are effective for analyzing censored cancer data.
- Simpler statistical models may adequately represent DCIS subtypes.
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