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Comparative validity of methods to select appropriate cutoff weight for probabilistic linkage without unique personal
Ying Zhu1, Chih-Ying Chen2, Yutaka Matsuyama1
1Department of Biostatistics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Pharmacoepidemiology and Drug Safety
|July 17, 2015
Summary
Probabilistic linkage without unique personal identifiers (UPI) can create valid data links for observational studies. Methods like histogram inspection and the odds formula aid in selecting optimal cutoffs for accurate record linkage.
Area of Science:
- Health Informatics
- Biostatistics
- Observational Database Studies
Background:
- Record linkage is crucial for enhancing data quality in observational studies.
- Probabilistic linkage handles data variations but can yield false positives.
- Unique personal identifiers (UPI) are often unavailable, complicating linkage.
Purpose of the Study:
- To evaluate the validity of probabilistic linkage without UPI.
- To assess different methods for selecting cutoff weights in probabilistic linkage.
Main Methods:
- Linked an implantable cardioverter defibrillator registry with Medicare inpatient files using anonymous, nonunique variables.
- Assessed three cutoff selection methods against a gold standard derived with UPI.
Main Results:
- 55% of records linked at cutoffs yielding a positive predictive value (PPV) of ≥90%.
- Histogram inspection provided optimal cutoff ranges.
- The duplicate and odds formula methods offered accurate PPV and cutoff estimations.
Conclusions:
- Probabilistic linkage without UPI is valid with appropriate cutoff selection.
- Histogram inspection, duplicate method, and odds formula aid cutoff selection when a gold standard is absent.
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