Evaluation of imputation strategies for multi-centre studies: Application to a large clinical pathology dataset

In general, storing and sharing datasets prior to any correction is a good practise, so that imputation can be performed on merged data if necessary.

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Publication Details

Journal
PLoS ONE
Published
2025-11-20
DOI
https://doi.org/10.1371/journal.pone.0335852
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.00
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article

Evaluation of imputation strategies for multi-centre studies: Application to a large clinical pathology dataset

Jeremy K. Nicholson, John C. Lindon, Julien Wist, Elaine Holmes et al.
PLoS ONE
Statistical Methods and Bayesian Inference
article

Evaluation of imputation strategies for multi-centre studies: Application to a large clinical pathology dataset

Jeremy K. Nicholson, John C. Lindon, Julien Wist, Elaine Holmes, Reika Masuda, Lucy Grigoroff, Janonna Kadyrov
article en

Abstract

In general, storing and sharing datasets prior to any correction is a good practise, so that imputation can be performed on merged data if necessary.

PLoS ONEVol. 20(11)
Murdoch University (AU), Imperial College London (GB), Universidad del Valle (CO)
Openalex Percentile: Top 67%
Statistical Methods and Bayesian Inference
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