A practical approach for incorporating dependence among fields in probabilistic record linkage

BMC Medical Informatics and Decision Making

Technical Advance

Siu L Hui¹  Huiping Xu¹  Joanne K Daggy¹  Shaun J Grannis²  Roland E Gamache² 

[1] Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA;Regenstrief Institute and Indiana University School of Medicine, Indianapolis, IN, USA;

关键词: Bayesian Information Criterion;    Conditional Independence;    Record Linkage;    Latent Class Model;    Health Information Exchange;   

DOI  :  10.1186/1472-6947-13-97

 received in 2012-12-05, accepted in 2013-08-20,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundMethods for linking real-world healthcare data often use a latent class model, where the latent, or unknown, class is the true match status of candidate record-pairs. This commonly used model assumes that agreement patterns among multiple fields within a latent class are independent. When this assumption is violated, various approaches, including the most commonly proposed loglinear models, have been suggested to account for conditional dependence.MethodsWe present a step-by-step guide to identify important dependencies between fields through a correlation residual plot and demonstrate how they can be incorporated into loglinear models for record linkage. This method is applied to healthcare data from the patient registry for a large county health department.ResultsOur method could be readily implemented using standard software (with code supplied) to produce an overall better model fit as measured by BIC and deviance. Finding the most parsimonious model is known to reduce bias in parameter estimates.ConclusionsThis novel approach identifies and accommodates conditional dependence in the context of record linkage. The conditional dependence model is recommended for routine use due to its flexibility for incorporating conditional dependence and easy implementation using existing software.

【 授权许可】

CC BY   
© Daggy et al.; licensee BioMed Central Ltd. 2013