Missing values in deduplication of electronic patient data

Systematic approaches to dealing with missing values in record linkage are still lacking. This article compares the ad-hoc treatment of unknown comparison values as 'unequal' with other and more sophisticated approaches. An empirical evaluation was conducted of the methods on real-world data as well as on simulated data based on them.

Cancer registry data and artificial data with increased numbers of missing values in a relevant variable are used for empirical comparisons. As a classification method, classification and regression trees were used. On the resulting binary comparison patterns, the following strategies for dealing with missingness are considered: imputation with unique values, sample-based imputation, reduced-model classification and complete-case induction. These approaches are evaluated according to the number of training data needed for induction and the F-scores achieved.

The evaluations reveal that unique value imputation leads to the best results. Imputation with zero is preferred to imputation with 0.5, although the latter shows the highest median F-scores. Imputation with zero needs considerably less training data, it shows only slightly worse results and simplifies the computation by maintaining the binary structure of the data.

The results support the ad-hoc solution for missing values 'replace NA by the value of inequality'. This conclusion is based on a limited amount of data and on a specific deduplication method. Nevertheless, the authors are confident that their results should be confirmed by other empirical analyses and applications.