Xu, HuipingLi, XiaochunZhang, ZuoyiGrannis, Shaun2024-01-162024-01-162022-11Xu, H., Li, X., Zhang, Z., & Grannis, S. (2022). Score Test for Assessing the Conditional Dependence in Latent Class Models and its Application to Record Linkage. Journal of the Royal Statistical Society Series C: Applied Statistics, 71(5), 1663–1687. https://doi.org/10.1111/rssc.12590https://hdl.handle.net/1805/38008The Fellegi–Sunter model has been widely used in probabilistic record linkage despite its often invalid conditional independence assumption. Prior research has demonstrated that conditional dependence latent class models yield improved match performance when using the correct conditional dependence structure. With a misspecified conditional dependence structure, these models can yield worse performance. It is, therefore, critically important to correctly identify the conditional dependence structure. Existing methods for identifying the conditional dependence structure include the correlation residual plot, the log-odds ratio check, and the bivariate residual, all of which have been shown to perform inadequately. Bootstrap bivariate residual approach and score test have also been proposed and found to have better performance, with the score test having greater power and lower computational burden. In this paper, we extend the score-test-based approach to account for different conditional dependence structures. Through a simulation study, we develop practical recommendations on the utilisation of the score test and assess the match performance with conditional dependence identified by the proposed method. Performance of the proposed method is further evaluated using a real-world record linkage example. Findings show that the proposed method leads to improved matching accuracy relative to the Fellegi–Sunter model.en-USPublisher Policyconditional dependenceFellegi–Sunter modellog-linear latent class modelpatient matchingArticle