Open AccessDealing with Missing Predictor Values When Applying Clinical Prediction Models
Author(s) -
Kristel J.M. Janssen,
Yvonne Vergouwe,
A. Rogier T. Donders,
Frank E. Harrell,
Qingxia Chen,
Diederick E. Grobbee,
Karel G.M. Moons
Publication year - 2009
Publication title -
clinical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.705
H-Index - 218
eISSN - 1530-8561
pISSN - 0009-9147
DOI - 10.1373/clinchem.2008.115345
Subject(s) - imputation (statistics) , missing data , statistics , discriminative model , predictive modelling , mathematics , computer science , artificial intelligence
Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with such missing values. We evaluated 6 strategies to deal with missing values.
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