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A structured decision-support framework for selecting imputation methods in clinical structured datasets: A secondary

Marziyeh Afkanpour1, Mehri Momeni2, Hamed Tabesh3

  • 1Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Student Research Committee, Mashhad University of Medical Science, Mashhad, Iran.

International Journal of Medical Informatics
|January 30, 2026
PubMed
Summary

Handling missing values in clinical data is crucial. This study introduces a framework to select appropriate imputation methods based on data characteristics and structure, enhancing analysis reliability.

Keywords:
Clinical tabular datasetData preprocessingDecision-support frameworkHealth careImputation methodsMissing dataPre-assumptions of imputation

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Area of Science:

  • Medical Informatics
  • Data Science
  • Biostatistics

Background:

  • Missing values are prevalent in clinical datasets, posing challenges to data analysis.
  • Inadequate handling of missing data can introduce bias and compromise research validity.
  • Imputation methods are essential for addressing missing data, but selection requires careful consideration of dataset specifics.

Purpose of the Study:

  • To propose a structured decision-support framework for selecting appropriate imputation methods.
  • To define key prerequisites for choosing imputation techniques in clinical data preprocessing.
  • To enhance the transparency and reproducibility of handling missing values in healthcare data.

Main Methods:

  • A secondary analysis of 69 studies from a systematic review was performed.
  • Domain experts identified and evaluated factors influencing imputation method selection.
  • Consensus was reached on critical factors, which were synthesized into a decision framework.

Main Results:

  • Nine essential factors for imputation method selection were identified.
  • Key factors include missing data characteristics (mechanism, pattern, ratio) and dataset attributes (type, role, distribution, correlation).
  • The ratio of missingness, variable role, and missing value mechanism were most influential.

Conclusions:

  • Understanding missing data characteristics and dataset structure is vital for appropriate imputation.
  • The developed framework offers an evidence-based checklist for preprocessing clinical datasets.
  • This approach improves transparency, reproducibility, and reliability in medical informatics.