A practical approach for incorporating dependence among fields in probabilistic record linkage
Joanne K Daggy1, Huiping Xu, Siu L Hui
1Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA. jdaggy2@iupui.edu.
BMC Medical Informatics and Decision Making
|September 5, 2013
Summary
This study introduces a new method to improve healthcare record linkage by accounting for dependencies between data fields. The approach enhances model accuracy and reduces bias in matching patient records.
Area of Science:
- Health Informatics
- Biostatistics
- Data Linkage
Background:
- Latent class models are standard for healthcare data linkage, assuming independence between fields within a class.
- Violations of this independence assumption can lead to inaccuracies in record linkage.
- Loglinear models are often used to address conditional dependence.
Purpose of the Study:
- To present a method for identifying and incorporating field dependencies into loglinear models for record linkage.
- To improve the accuracy and reduce bias in linking real-world healthcare data.
Main Methods:
- A step-by-step guide to identify field dependencies using correlation residual plots.
- Incorporation of identified dependencies into loglinear models for record linkage.
- Application to healthcare data from a county health department patient registry.
Main Results:
- The proposed method, implemented with standard software, significantly improves model fit (measured by BIC and deviance).
- Identifying and accommodating conditional dependence leads to more parsimonious models.
- Reduced bias in parameter estimates is achieved through more accurate modeling.
Conclusions:
- A novel approach effectively identifies and accommodates conditional dependence in healthcare record linkage.
- The conditional dependence model is recommended for routine use due to its flexibility and ease of implementation.
- This method enhances the reliability of linking real-world healthcare data.
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