Article

Applications of Bayesian Shrinkage Prior Models in Clinical Research with Categorical Responses

2021-01-05

Abstract excerpt

<h4>Background: </h4> Prediction and classiﬁcation algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions like diabetes, colon cancer, and Alzheimer’s disease. Developing accurate prediction and classiﬁcation methods have implications for personalized medicine. Building an excellent predictive model involves selecting features that are most signiﬁcantly associ...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8d572ffb-52a6-5313-9a10-ef6cb563fd76
DOI: 10.21203/rs.3.rs-137866/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Applications of Bayesian Shrinkage Prior Models in Clinical Research with Categorical ResponsesDOI 10.21203/rs.3.rs-137866/v1Read

Count data models and Bayesian shrinkage priors with real-world data applicationsSemantic proximity Read

High-dimensional Bayesian phenotype classification and model selection using genomic predictorsSemantic proximity Read

A Physiology-Aware, Orthogonalized, Calibration-First Machine-Learning Diabetes Risk Model Robust to Population ShiftSemantic proximity Read

Sparse prediction informed by genetic annotations using the logit normal prior for Bayesian regression tree ensembles.Semantic proximity Read

Bayesian Multivariate Genetic Analysis Boosts Translational InsightsSemantic proximity Read

Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects.Semantic proximity Read

Comparison of Bayesian approaches for developing prediction models in rare disease: application to the identification of patients with Maturity-Onset Diabetes of the YoungSemantic proximity Read

Clinical Prediction Models: Foundational ConceptsSemantic proximity Read

Shrinkage methods for variable selection and prediction with applications to genetic dataSemantic proximity Read

An efficient bayesian method for predicting clinical outcomes from genome-wide data.Semantic proximity Read

Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicineSemantic proximity Read

Univariate shrinkage in the cox model for high dimensional data.Semantic proximity Read