Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy

Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy

Abstract Background The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice. Methods We utilise a flexible...

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Bibliographic Details
Main Authors: Pedro Cardoso, John M. Dennis, Jack Bowden, Beverley M. Shields, Trevelyan J. McKinley, the MASTERMIND Consortium
Format: Article
Language:English
Published: BMC 2024-01-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Dirichlet process mixture model
Treatment selection model
Precision medicine
Type 2 diabetes
Bayesian modelling
Online Access:https://doi.org/10.1186/s12911-023-02400-3

Internet

https://doi.org/10.1186/s12911-023-02400-3

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