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Statistical methods for retrospective harmonization of longitudinal epidemiological data: a scoping review
Jiumeng Zhang1, Jordan Behrendt2, Tanja Schultz2
1Department of Statistical Methods in Epidemiology, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, 28359, Bremen, Germany.
European Journal of Epidemiology
|May 28, 2026
Summary
Data harmonization is crucial for combining longitudinal data. This review identifies three statistical methods—distribution-based, proportion score, and latent variable models—for retrospective data harmonization, guiding researchers in method selection.
Area of Science:
- Epidemiology
- Biostatistics
- Data Science
Background:
- Joint cohort analyses require harmonized longitudinal data.
- Retrospective harmonization of participant-level longitudinal data is complex.
- Existing methods for data harmonization vary in applicability.
Purpose of the Study:
- To identify and contrast statistical methods for retrospective harmonization of longitudinal data.
- To provide guidance for researchers selecting harmonization methods.
- To highlight the need for automated harmonization tools.
Main Methods:
- Scoping review following PRISMA-ScR guidelines.
- Inclusion of studies describing statistical methods for retrospective longitudinal data harmonization.
- Identification and categorization of harmonization techniques.
Main Results:
- Three main statistical harmonization methods were identified: distribution-based, proportion score model, and latent variable models.
- Method suitability depends on variable measurement scales and target variable type.
- Current methods do not extensively utilize machine learning for automated harmonization.
Conclusions:
- A roadmap is provided to aid researchers in selecting appropriate statistical harmonization methods.
- Handling variables collected in only a subset of studies is a key consideration.
- Development of novel, automated tools for data harmonization is needed.
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