Browsing by Author "Liu, Juxin"

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    Latent variable mixture models to test for differential item functioning: a population-based analysis
    (2017-05-15) Wu, Xiuyun; Sawatzky, Richard; Hopman, Wilma; Mayo, Nancy; Sajobi, Tolulope T; Liu, Juxin; Prior, Jerilynn; Papaioannou, Alexandra; Josse, Robert G; Towheed, Tanveer; Davison, K. S; Lix, Lisa M
    Abstract Background Comparisons of population health status using self-report measures such as the SF-36 rest on the assumption that the measured items have a common interpretation across sub-groups. However, self-report measures may be sensitive to differential item functioning (DIF), which occurs when sub-groups with the same underlying health status have a different probability of item response. This study tested for DIF on the SF-36 physical functioning (PF) and mental health (MH) sub-scales in population-based data using latent variable mixture models (LVMMs). Methods Data were from the Canadian Multicentre Osteoporosis Study (CaMos), a prospective national cohort study. LVMMs were applied to the ten PF and five MH SF-36 items. A standard two-parameter graded response model with one latent class was compared to multi-class LVMMs. Multivariable logistic regression models with pseudo-class random draws characterized the latent classes on demographic and health variables. Results The CaMos cohort consisted of 9423 respondents. A three-class LVMM fit the PF sub-scale, with class proportions of 0.59, 0.24, and 0.17. For the MH sub-scale, a two-class model fit the data, with class proportions of 0.69 and 0.31. For PF items, the probabilities of reporting greater limitations were consistently higher in classes 2 and 3 than class 1. For MH items, respondents in class 2 reported more health problems than in class 1. Differences in item thresholds and factor loadings between one-class and multi-class models were observed for both sub-scales. Demographic and health variables were associated with class membership. Conclusions This study revealed DIF in population-based SF-36 data; the results suggest that PF and MH sub-scale scores may not be comparable across sub-groups defined by demographic and health status variables, although effects were frequently small to moderate in size. Evaluation of DIF should be a routine step when analysing population-based self-report data to ensure valid comparisons amongst sub-groups.
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    Mixture Model Analysis with Misclassified Covariates: Methods and Applications
    (2024-09-20) Zhang, Ruixuan; Shen, Hua; Kopciuk, Karen; Liu, Juxin; Lu, Xuewen
    Mixture models are crucial for analyzing data with underlying sub-populations. Misclassification introduces discrepancies between observations and true values, which can severely bias parameter estimation, especially for mixture models when subgroups are not easily identifiable. We propose a method to enhance parameter estimation within the framework of mixture models, and mitigate the impact of misclassified covariates by utilizing them as surrogates in the Expectation-Maximization algorithm. Simulations consider both non-differential and differential misclassification with varying sample sizes, sensitivities, specificities, subgroup proportions and misclassified covariate proportions. Results demonstrate robust performance compared to naive or ad hoc approaches ignoring the misclassification issue, even under challenging conditions, such as low sensitivity and specificity for the misclassified covariate, or small sample sizes. For illustration, we apply our method to the 2015 Behavioral Risk Factor Surveillance System data. We conclude with a discussion of the implications of our findings and directions for future research.