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Construction and Assessment of Prediction Rules for Binary Outcome in the Presence of Missing Predictor Data Using Multiple Imputation: Theoretical Perspective and Data-based Evaluation by Mertens, B. J. A.; Banzato, E.; de Wreede, L. C. is a scholarly article available to read on EtoBox.

What is Construction and Assessment of Prediction Rules for Binary Outcome in the Presence of Missing Predictor Data Using Multiple Imputation: Theoretical Perspective and Data-based Evaluation about?

We investigate the problem of calibration and assessment of predictive rules in prognostic designs when missing values are present in the predictors. Our paper has two key objectives which are entwined. The first is to investigate how the calibration of the prediction rule can be combined with the use of multiple imputation to account for missing predictor observations. The second objective is to propose such methods that can be implemented with current multiple imputation software, while allowing for unbiased predictive assessment through validation on new observations for which outcome is not yet available. To inform the definition of methodology, we commence with a review of the theoretical background of multiple imputation as a model estimation approach as opposed to a purely algorithmic description. We specifically contrast application of multiple imputation for parameter (effect) estimation with predictive calibration. Based on this review, two approaches are formulated, of which the second utilizes application of the classical Rubin's rules for parameter estimation, while the first approach averages probabilities from models fitted on single imputations to directly approxima

Author

Mertens, B. J. A.; Banzato, E.; de Wreede, L. C.

Published

2018

Language

EN