Application of structural equation modeling in two independent problems in health sciences

Abstract

Structural Equation Modeling (SEM) is a powerful multivariate statistical technique that enables researchers to analyze complex relationships among observed and latent variables. While its origins lie in the social sciences, SEM has gained growing attention in medical and healthcare research due to its ability to model multifactorial phenomena and uncover latent structures underlying observable clinical outcomes. This thesis explores the integration of SEM into various facets of health sciences, particularly in the evaluation and prediction of cardiovascular disease risk using non-invasive imaging and clinical indicators. By utilizing latent constructs such as life-design behavior, biochemical markers, and vascular features—derived from ultrasound-based texture analysis of the carotid artery—SEM models are developed to examine the direct and indirect effects of these variables on cardiovascular health. Through a series of case studies, including preliminary work on the intima-media complex (IMC) segmentation and more advanced modeling in published research (for example, Section 3.2 and Section 3.3), the thesis demonstrates the effectiveness of SEM in capturing both measurement and structural relationships in healthcare data. The methodology emphasizes the importance of confirmatory factor analysis, path analysis, and the testing of model fit indices to ensure robustness and interpretability. Applications discussed include modeling the influence of behavioral, demographic, and physiological factors on disease risk, as well as evaluating the systemic structure of healthcare delivery systems, such as the General Health System of Cyprus. Ultimately, this thesis aims to showcase SEM not just as a methodological choice, but as a strategic approach to advancing medical research through data-driven, theoretically grounded models. The results underline SEM's potential to improve diagnostic tools, support clinical decision-making, and contribute to the development of personalized healthcare strategies.