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Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a

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Understanding missing data is key in clinical research. This review maps imputation techniques by data characteristics to guide healthcare analysts in choosing the best methods for reliable outcomes.

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Clinical datasetImputation methodsMechanism of missingnessMissing ratioMissing valuesPattern of missingnessSimulation study

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Area of Science:

  • Data Science
  • Biostatistics
  • Health Informatics

Background:

  • Accurate handling of missing values in clinical datasets is critical for reliable research outcomes and informed decision-making.
  • Increasing data complexity necessitates effective imputation techniques to address missing data.
  • This study focuses on guiding health analysts in selecting appropriate imputation methods based on dataset characteristics.

Purpose of the Study:

  • To systematically review and introduce various imputation techniques for missing values in clinical datasets.
  • To develop an evidence map recommending suitable imputation methods based on missing data's mechanism, pattern, and ratio.
  • To provide a guideline for choosing appropriate imputation methods during data preprocessing for structured clinical datasets.

Main Methods:

  • Systematic literature search across PubMed, Web of Science, Scopus, and IEEE Xplore up to September 20, 2023.
  • Analysis focused on missing data mechanism, pattern, ratio, and imputation strategies.
  • Synthesis of insights to construct an evidence map for recommending imputation methods.

Main Results:

  • 58 out of 2955 articles were included in the analysis.
  • Conventional statistical methods were used in 45% of studies.
  • Machine learning/deep learning methods were applied in 31%, and hybrid techniques in 24% of studies.

Conclusions:

  • Choosing imputation techniques based on missing data characteristics is essential for clinical datasets.
  • Accurate imputation enhances data quality, reusability, and supports precise medical decision-making.
  • This review offers a guideline for selecting optimal imputation methods in data preprocessing stages.