Browsing by Subject "Path analysis"

Now showing 1 - 3 of 3
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    Distal and Proximal Influences on Self-Reported Oral Pain and Self-Rated Oral Health Status in Saudi Arabia: Retrospective Study Using a 2017 Nationwide Database
    (JMIR, 2024-12-20) Abogazalah, Naif; Yiannoutsos, Constantin; Soto-Rojas, Armando E.; Bindayeld, Naif; Yepes, Juan F.; Martinez Mier, Esperanza Angeles; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background: Oral health significantly influences overall well-being, health care costs, and quality of life. In Saudi Arabia, the burden of oral diseases, such as dental caries and periodontal disease, has increased over recent decades, driven by various lifestyle changes. Objective: To explore the associations between proximal (direct) and distal (indirect) influences that affect oral pain (OP) and self-rated oral health (SROH) status in the Kingdom of Saudi Arabia (KSA) using an adapted conceptual framework. Methods: This retrospective cross-sectional study used data from a national health survey conducted in KSA in 2017. The sample included adults (N=29,274), adolescents (N=9910), and children (N=11,653). Sociodemographic data, health characteristics, and access to oral health services were considered distal influences, while frequency and type of dental visits, tooth brushing frequency, smoking, and consumption of sweets and soft drinks were considered proximal influences. Path analysis modeling was used to estimate the direct, indirect, and total effects of proximal and distal influences on OP and SROH status. Results: The mean age of adult respondents was 42.2 years; adolescents, 20.4 years; and children, 10.58 years. Despite OP reports from 39% of children, 48.5% of adolescents, and 47.1% of adults, over 87% across all groups rated their oral health as good, very good, or excellent. A higher frequency of tooth brushing showed a strong inverse relationship with OP and a positive correlation with SROH (P<.001). Frequent dental visits were positively associated with OP and negatively with SROH (P<.001). Sweet consumption increased OP in adolescents (β=0.033, P=.007) and negatively affected SROH in children (β=-0.086, P<.001), adolescents (β=-0.079, P<.001), and adults (β=-0.068, P<.001). Soft drink consumption, however, was associated with lower OP in adolescents (β=-0.034, P=.005) and improved SROH in adolescents (β=0.063, P<.001) and adults (β=0.068, P<.001). Smoking increased OP in adults (β=0.030, P<.001). Distal influences like higher education were directly linked to better SROH (β=0.046, P=.003) and less OP (indirectly through tooth brushing, β=-0.004, P<.001). For children, high household income correlated with less OP (β=-0.030, P=.02), but indirectly increased OP through other pathways (β=0.024, P=.003). Lack of access was associated with negative oral health measures (P<.001). Conclusions: Among the KSA population, OP and SROH were directly influenced by many proximal and distal influences that had direct, indirect, or combined influences on OP and SROH status.
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    Identifying brain hierarchical structures associated with Alzheimer’s disease using a regularized regression method with tree predictors
    (Oxford University Press, 2023) Zhao, Yi; Wang, Bingkai; Liu, Chin-Fu; Faria, Andreia V.; Miller, Michael I.; Caffo, Brian S.; Luo, Xi; Biostatistics and Health Data Science, School of Medicine
    Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer’s disease outcomes. In this study, we propose an ℓ1-type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2-norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.
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    Pathway Lasso: Pathway Estimation and Selection with High-Dimensional Mediators
    (International Press, 2022) Zhao, Yi; Luo, Xi; Biostatistics, School of Public Health
    In many scientific studies, it becomes increasingly important to delineate the pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate the pathway effects, commonly expressed as the product of coefficients. However, it becomes unstable and computationally challenging to fit such models with high-dimensional mediators. This paper proposes a sparse mediation model using a regularized SEM approach, where sparsity means that a small number of mediators have a nonzero mediation effect between a treatment and an outcome. To address the model selection challenge, we innovate by introducing a new penalty called Pathway Lasso. This penalty function is a convex relaxation of the non-convex product function for the mediation effects, and it enables a computationally tractable optimization criterion to estimate and select pathway effects simultaneously. We develop a fast ADMM-type algorithm to compute the model parameters, and we show that the iterative updates can be expressed in closed form. We also prove the asymptotic consistency of our Pathway Lasso estimator for the mediation effect. On both simulated data and an fMRI data set, the proposed approach yields higher pathway selection accuracy and lower estimation bias than competing methods.