Exploring the Complexity of Real‐World Health Data Record Linkage—An Exemplary Study Linking Cancer Registry and Claims Data

Linkage of German claims and cancer registry data based on quasi-identifiers does result in insufficient linkage quality since subjects cannot be uniquely identified. It is advisable to use unique identifiers from a subsample, if available, to derive informed linkage algorithms for the entire sample. In this case, the machine learning technique gradient boosting has been found to outperform other methods.

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Publication Details

Journal
Pharmacoepidemiology and Drug Safety
Published
2025-03-25
DOI
https://doi.org/10.1002/pds.70120
Primary Topic
Data Quality and Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploring the Complexity of Real‐World Health Data Record Linkage—An Exemplary Study Linking Cancer Registry and Claims Data

Timm Intemann, Bianca Kollhorst, Nadja Lendle
Pharmacoepidemiology and Drug Safety
Data Quality and Management
article

Exploring the Complexity of Real‐World Health Data Record Linkage—An Exemplary Study Linking Cancer Registry and Claims Data

Timm Intemann, Bianca Kollhorst, Nadja Lendle
article en

Abstract

Linkage of German claims and cancer registry data based on quasi-identifiers does result in insufficient linkage quality since subjects cannot be uniquely identified. It is advisable to use unique identifiers from a subsample, if available, to derive informed linkage algorithms for the entire sample. In this case, the machine learning technique gradient boosting has been found to outperform other methods.

Pharmacoepidemiology and Drug SafetyVol. 34(4)
Leibniz Institute for Prevention Research and Epidemiology - BIPS (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 94%
Data Quality and Management
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