A comparison of model selection methods for prediction in the presence of multiply imputed data

DOI10.1002/bimj.201700232zbMath1419.62456OpenAlexW2898355413WikidataQ57795751 ScholiaQ57795751MaRDI QIDQ4967098

Ronald B. Geskus, Le Thi Phuong Thao

Publication date: 2 July 2019

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201700232

zbMATH Keywords

predictionvariable selectionLassomultiply imputed datastacked data

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (1)

Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection

Uses Software

• glmnet
• MICE
• AppliedPredictiveModeling

Cites Work

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• Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis
• Clinical prediction models. A practical approach to development, validation, and updating.
• Applied Predictive Modeling
• On the existence of maximum likelihood estimates in logistic regression models
• A simulation based method for assessing the statistical significance of logistic regression models after common variable selection procedures
• Variable selection when missing values are present: a case study
• Bootstrap Methods for Developing Predictive Models
• Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect

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