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Updated: Jun 21, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Development and validation of a prediction model with missing predictor data: a practical approach.

Yvonne Vergouwe¹, Patrick Royston, Karel G M Moons

¹Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Str 6.131, P.O. Box 85500, 3508 GA, Utrecht, The Netherlands. y.vergouwe@umcutrecht.nl

Journal of Clinical Epidemiology

|July 15, 2009

Summary

This summary is machine-generated.

Developing and validating clinical prediction models with multiply imputed data is feasible. This study demonstrates a practical approach for handling missing data in deep venous thrombosis prediction models.

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Last Updated: Jun 21, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Medical Informatics
Biostatistics
Clinical Epidemiology

Background:

Clinical prediction models are essential for diagnosis and prognosis.
Handling missing data is a critical challenge in model development.
Multiple imputation (MI) is a robust technique for addressing missing data.

Purpose of the Study:

To outline a practical workflow for developing and validating clinical prediction models using multiply imputed data.
To demonstrate the application of MI in the context of a deep venous thrombosis (DVT) diagnostic model.
To evaluate different methods for predictor selection and continuous predictor transformation with MI.

Main Methods:

Development and validation of a DVT diagnostic model using primary care patient data.
Multiple Imputation by Chained Equations (MICE) was used for data imputation (10 imputations).
Three distinct methods were employed for predictor selection and continuous predictor transformation.

Main Results:

The three methods for predictor selection and continuous predictor transformation yielded comparable results.
Rubin's rules were effectively applied for estimating regression coefficients and performance measures post-imputation.
The proposed approach facilitated straightforward model validation with multiply imputed datasets.

Conclusions:

A practical and effective approach for clinical prediction model development and validation with multiply imputed data is presented.
This methodology ensures robust model performance assessment when dealing with missing predictor values.
The findings support the routine use of MI in the development of reliable clinical prediction models.