2020/03/12 by Shovanur Haque, Haque, Shovanur, Kerrie Mengersen +1
Computer Science · Decision Sciences · Mathematics · Medicine · #Applications (stat.AP) #Computation (stat.CO) #Data Quality and Management #Data-Driven Disease Surveillance #Databases (cs.DB) #FOS: Computer and information sciences #cs.DB #stat.AP #stat.CO
paper · pdf · doi:10.48550/arxiv.2003.05686
24 pages, 6 figures. arXiv admin note: text overlap with arXiv:1901.04779
openalex publication_date 2020/03/12 · arxiv created 2020/11/23 · arxiv updated 2020/11/26 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
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.