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Functional Structural Equation Model.

ABSTRACT: In this article, we introduce a functional structural equation model for estimating directional relations from multivariate functional data. We decouple the estimation into two major steps: directional order determination and selection through sparse functional regression. We first propose a score function at the linear operator level, and show that its minimization can recover the true directional order when the relation between each function and its parental functions is nonlinear. We then develop a sparse functional additive regression, where both the response and the multivariate predictors are functions and the regression relation is additive and nonlinear. We also propose strategies to speed up the computation and scale up our method. In theory, we establish the consistencies of order determination, sparse functional additive regression, and directed acyclic graph estimation, while allowing both the dimension of the Karhunen-Loéve expansion coefficients and the number of random functions to diverge with the sample size. We illustrate the efficacy of our method through simulations, and an application to brain effective connectivity analysis.

SUBMITTER: Lee KY

PROVIDER: S-EPMC9017995 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Functional Structural Equation Model.

Lee Kuang-Yao KY Li Lexin L

Journal of the Royal Statistical Society. Series B, Statistical methodology 20220321 2

In this article, we introduce a functional structural equation model for estimating directional relations from multivariate functional data. We decouple the estimation into two major steps: directional order determination and selection through sparse functional regression. We first propose a score function at the linear operator level, and show that its minimization can recover the true directional order when the relation between each function and its parental functions is nonlinear. We then dev ...[more]

PMID: 35450387

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Project description:Background/Study Context: Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations.MethodsThe Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status.ResultsPreconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA < .05; all CFI > .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p < .005).ConclusionsEmpirical data confirm the hypothesized ACTIVE conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be different from those with the greatest chance of transfer to real-world activities.

Project description:Aims Living alone, a reality in an increasing number of older adults recently, is a risk factor for low quality of life. This study identified the predictors of quality of life in older adults living alone based on mental health and the International Classification of Functioning, Disability, and Health. Methods This secondary data analysis used information from the 2018 Assessing the Requirements of Customized Health Care and Daily Living Support Services survey (N = 1,022), collected from adults aged ≥ 65 living alone in Siheung City, South Korea, from August to October 2018. The exogenous variables were body functions (hand grip strength, timed “up and go” test score, and body mass index), daily living activities (Korean Instrumental Activities of the Daily Living Scale), social activity participation (social activity engagement, neighbor contacts, and family contacts), and participation in economic activity (frequency). The endogenous variables were mental health (Geriatric Depression Scale Short Form—Korean Version and UCLA Loneliness Scale) and quality of life (EuroQoL-5 Dimension-3 Level and EuroQoL-Visual Analog Scale). Results After modifying the hypothetical model, which had failed to satisfy the recommended fitness level, the (modified) model had good fitness indices Q (CMIN / df) 2.90, GFI 1, AGFI 1, RMSEA 0.04, CFI 0.90 and PCFI 0.53. Of the nine pathways of the modified model, five were statistically significant. Quality of life was affected by body functions, daily living activities, social activity participation, and mental health. These variables explained 68.2% of the factors affecting quality of life. Conclusions By highlighting the role of mental health, this model provides a useful framework for improving the quality of life of older adults who live alone and function at various levels in the community. Focusing on advancing mental health through body functions, daily living activities, and social activity participation can improve quality of life.

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Functional Structural Equation Model.

Publications

Functional Structural Equation Model.

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