Browsing by Subject "conditional dependence"

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    Incorporating conditional dependence in latent class models for probabilistic record linkage: Does it matter?
    (ims, 2019) Xu, Huiping; Li, Xiaochun; Shen, Changyu; Hui, Siu L.; Grannis, Shaun; Family Medicine, School of Medicine
    The conditional independence assumption of the Felligi and Sunter (FS) model in probabilistic record linkage is often violated when matching real-world data. Ignoring conditional dependence has been shown to seriously bias parameter estimates. However, in record linkage, the ultimate goal is to inform the match status of record pairs and therefore, record linkage algorithms should be evaluated in terms of matching accuracy. In the literature, more flexible models have been proposed to relax the conditional independence assumption, but few studies have assessed whether such accommodations improve matching accuracy. In this paper, we show that incorporating the conditional dependence appropriately yields comparable or improved matching accuracy than the FS model using three real-world data linkage examples. Through a simulation study, we further investigate when conditional dependence models provide improved matching accuracy. Our study shows that the FS model is generally robust to the conditional independence assumption and provides comparable matching accuracy as the more complex conditional dependence models. However, when the match prevalence approaches 0% or 100% and conditional dependence exists in the dominating class, it is necessary to address conditional dependence as the FS model produces suboptimal matching accuracy. The need to address conditional dependence becomes less important when highly discriminating fields are used. Our simulation study also shows that conditional dependence models with misspecified dependence structure could produce less accurate record matching than the FS model and therefore we caution against the blind use of conditional dependence models.
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    Score Test for Assessing the Conditional Dependence in Latent Class Models and its Application to Record Linkage
    (Oxford, 2022-11) Xu, Huiping; Li, Xiaochun; Zhang, Zuoyi; Grannis, Shaun; Biostatistics and Health Data Science, School of Medicine
    The 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.