Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage

Abstract

Objective: To gain insight into the performance of deterministic record linkage (DRL) vs. probabilistic record linkage (PRL) strategies under different conditions by varying the frequency of registration errors and the amount of discriminating power.

Study design and setting: A simulation study in which data characteristics were varied to create a range of realistic linkage scenarios. For each scenario, we compared the number of misclassifications (number of false nonlinks and false links) made by the different linking strategies: deterministic full, deterministic N-1, and probabilistic.

Results: The full deterministic strategy produced the lowest number of false positive links but at the expense of missing considerable numbers of matches dependent on the error rate of the linking variables. The probabilistic strategy outperformed the deterministic strategy (full or N-1) across all scenarios. A deterministic strategy can match the performance of a probabilistic approach providing that the decision about which disagreements should be tolerated is made correctly. This requires a priori knowledge about the quality of all linking variables, whereas this information is inherently generated by a probabilistic strategy.

Conclusion: PRL is more flexible and provides data about the quality of the linkage process that in turn can minimize the degree of linking errors, given the data provided.