Practical guidance for validating the predictive performance in the presence of missing data: a guide for the clinical researcher

This paper provides practical guidance and a structured R workflow to support clinical researchers in combining internal validation and imputation methods when building reliable prediction models.

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Publication Details

Journal
Journal of Clinical Epidemiology
Published
2026-01-22
DOI
https://doi.org/10.1016/j.jclinepi.2026.112159
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.00
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article

Practical guidance for validating the predictive performance in the presence of missing data: a guide for the clinical researcher

H.J.S. de Grooth, O.L. Cremer, V.M.T. de Jong, N. De Mul et al.
Journal of Clinical Epidemiology
Statistical Methods and Bayesian Inference
article

Practical guidance for validating the predictive performance in the presence of missing data: a guide for the clinical researcher

H.J.S. de Grooth, O.L. Cremer, V.M.T. de Jong, N. De Mul, S. el Bouhaddani
article en

Abstract

This paper provides practical guidance and a structured R workflow to support clinical researchers in combining internal validation and imputation methods when building reliable prediction models.

Journal of Clinical EpidemiologyVol. 192
King Saud bin Abdulaziz University for Health Sciences (SA), Utrecht University (NL), Heidelberg University (DE), University Hospital Heidelberg (DE), University Medical Center Utrecht (NL)
Openalex Percentile: Top 83%
Statistical Methods and Bayesian Inference
DOIOpen Access PDFOpenAlex
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Practical guidance for validating the predictive performance in the presence of missing data: a guide for the clinical researcher — H.J.S. de Grooth, O.L. Cremer, et al. · Journal of Clinical Epidemiology (2026) | TGRS Research Map | TGRS