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DOI	10.1186/s12874-022-01560-6
	Applications of Bayesian shrinkage prior models in clinical research with categorical responses
	Bhattacharyya, Arinjita; Pal, Subhadip; Mitra, Riten; Rai, Shesh
通讯作者	Rai, S
来源期刊	BMC MEDICAL RESEARCH METHODOLOGY
EISSN	1471-2288
出版年	2022
卷号	22 期号:1
英文摘要	Background Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions such as diabetes, colon cancer, and Alzheimer's disease. Developing accurate prediction and classification methods benefits personalized medicine. Building an excellent predictive model involves selecting the features that are most significantly associated with the outcome. These features can include several biological and demographic characteristics, such as genomic biomarkers and health history. Such variable selection becomes challenging when the number of potential predictors is large. Bayesian shrinkage models have emerged as popular and flexible methods of variable selection in regression settings. This work discusses variable selection with three shrinkage priors and illustrates its application to clinical data such as Pima Indians Diabetes, Colon cancer, ADNI, and OASIS Alzheimer's real-world data. Methods A unified Bayesian hierarchical framework that implements and compares shrinkage priors in binary and multinomial logistic regression models is presented. The key feature is the representation of the likelihood by a Polya-Gamma data augmentation, which admits a natural integration with a family of shrinkage priors, specifically focusing on Horseshoe, Dirichlet Laplace, and Double Pareto priors. Extensive simulation studies are conducted to assess the performances under different data dimensions and parameter settings. Measures of accuracy, AUC, brier score, L1 error, cross-entropy, and ROC surface plots are used as evaluation criteria comparing the priors with frequentist methods as Lasso, Elastic-Net, and Ridge regression. Results All three priors can be used for robust prediction on significant metrics, irrespective of their categorical response model choices. Simulation studies could achieve the mean prediction accuracy of 91.6% (95% CI: 88.5, 94.7) and 76.5% (95% CI: 69.3, 83.8) for logistic regression and multinomial logistic models, respectively. The model can identify significant variables for disease risk prediction and is computationally efficient. Conclusions The models are robust enough to conduct both variable selection and prediction because of their high shrinkage properties and applicability to a broad range of classification problems.
英文关键词	Shrinkage priors Logistic regression Horseshoe Dirichlet Laplace MCMC Polya-Gamma Multinomial ADNI Pima Data augmentation
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000788612600001
WOS关键词	VARIABLE SELECTION ; BREAST-CANCER ; REGRESSION ; BINARY ; EXPRESSION ; ESTIMATOR ; INFERENCE ; SAMPLER ; HER2
WOS类目	Health Care Sciences & Services
WOS研究方向	Health Care Sciences & Services
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392002
推荐引用方式 GB/T 7714	Bhattacharyya, Arinjita,Pal, Subhadip,Mitra, Riten,et al. Applications of Bayesian shrinkage prior models in clinical research with categorical responses[J],2022,22(1).
APA	Bhattacharyya, Arinjita,Pal, Subhadip,Mitra, Riten,&Rai, Shesh.(2022).Applications of Bayesian shrinkage prior models in clinical research with categorical responses.BMC MEDICAL RESEARCH METHODOLOGY,22(1).
MLA	Bhattacharyya, Arinjita,et al."Applications of Bayesian shrinkage prior models in clinical research with categorical responses".BMC MEDICAL RESEARCH METHODOLOGY 22.1(2022).