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

Clinical Snapshot

Key Findings

• Effect Size: Missingness ratio identified as most influential factor

• Primary Outcome: Nine key factors identified for imputation method selection

• Nnt Or Sensitivity: Framework sensitivity and specificity not assessed

• Confidence Interval: Not reported

Clinical Application

High feasibility - provides checklist-based approach for routine implementation Highly relevant for Australian health data initiatives including My Health Record, clinical registries, and research datasets. Aligns with NHMRC data management guidelines and could support TGA regulatory submissions requiring robust data handling Healthcare researchers and data scientists working with clinical structured datasets

Abstract

OBJECTIVE: Missing values are a common challenge in healthcare data analysis, and inadequate handling can introduce bias and undermine the validity of findings. Imputation methods offer a practical solution, but selecting an appropriate approach depends on multiple dataset-specific factors. This study proposes a structured decision-support framework that defines key prerequisites for choosing suitable imputation methods during the preprocessing of clinically structured datasets. METHODS: A secondary analysis of a previous systematic review was conducted, covering 69 studies to identify factors influencing imputation method selection. Domain experts evaluated assumptions regarding missing data characteristics and dataset structure, reaching consensus on the most relevant factors. These factors were synthesized into a structured framework designed to guide systematic and transparent imputation method selection in clinical data preprocessing workflows. RESULTS: Nine key factors were identified as essential for determining an appropriate imputation method. These include missing data characteristics, mechanism, pattern, and ratio and dataset attributes such as data type, variable role, distribution, and correlation. The ratio of missingness was the most influential factor, followed by variable role and missing value mechanism. Most studies emphasized the combined importance of both missing data properties and dataset features in imputation selection. CONCLUSIONS: Understanding the characteristics of missing values and dataset structure is crucial for selecting appropriate imputation methods. The proposed structured decision-support framework provides an evidence-based checklist to enhance transparency, reproducibility, and reliability in preprocessing clinical datasets within medical informatics workflows.

References

1. 1.Afkanpour, M., Momeni, M., & Tabesh, H. (2026). A structured decision-support framework for selecting imputation methods in clinical structured datasets: A secondary analysis. International Journal of Medical Informatics, 106298. https://doi.org/10.1016/j.ijmedinf.2026.106298