Assessing the accuracy of individual link with varying block sizes and cut-off values using MaCSim approach

TL;DR

This paper evaluates the accuracy of record linkage methods using a Markov Chain Monte Carlo simulation to optimize block sizes and cut-off values, aiming to minimize false discovery and false negative rates.

Contribution

It introduces a MaCSim approach for assessing individual link accuracy with varying parameters, aiding optimal linkage configuration.

Findings

MaCSim effectively estimates linkage accuracy.

Optimal block size and cut-off values reduce error rates.

Synthetic data analysis shows promising results.

Abstract

Record linkage is the process of matching together records from different data sources that belong to the same entity. Record linkage is increasingly being used by many organizations including statistical, health, government etc. to link administrative, survey, and other files to create a robust file for more comprehensive analysis. Therefore, it becomes necessary to assess the ability of a linking method to achieve high accuracy or compare between methods with respect to accuracy. In this paper, we evaluate the accuracy of individual link using varying block sizes and different cut-off values by utilizing a Markov Chain based Monte Carlo simulation approach (MaCSim). MaCSim utilizes two linked files to create an agreement matrix. The agreement matrix is simulated to generate re-sampled versions of the agreement matrix. A defined linking method is used in each simulation to link the files and the accuracy of the linking method is assessed. The aim of this paper is to facilitate optimal choice of block size and cut-off value to achieve high accuracy in terms of minimizing average False Discovery Rate and False Negative Rate. The analyses have been performed using a synthetic dataset provided by the Australian Bureau of Statistics (ABS) and indicated promising results.