Innovative Statistical and Computational Approaches for Multi-modal Data in Ophthalmology Research

Ophthalmology and vision science are crucial in the medical field due to their fundamental role in daily life and overall well-being. They enable individuals to interact with their environment, perform essential tasks, and connect with others. Vision health is closely linked to cognitive development, quality of life, and the early detection of systemic diseases. By prioritizing vision care, we enhance a vital aspect of human health, promoting independence and improving outcomes across various life stages. Research in ophthalmology and vision science seeks to uncover the intricate mechanisms underlying the structure and function of the eye in health, disease progression, and potential treatment effects. Such studies often generate comprehensive datasets, presenting significant challenges in statistical analysis. These challenges include: (i) managing diverse data types, such as imaging data (OCT and MRI), functional and structural measures, metabolomics/genomic data, and electronic health records; (ii) addressing complex data structures, including inter-eye correlations, repeated measurements over time and imbalanced data; and (iii) handling complications from irregularly spaced visits, missing data, and the integration of time-varying and static covariate effects. Overcoming these challenges requires sophisticated statistical modeling and the development of specialized methods tailored to the complexities of ophthalmic research. This session aims to bring together biostatisticians who specialize in the analysis of complex ophthalmic data. The primary objectives are to discuss the development of innovative statistical and computational methods and novel computational tools in ophthalmic research. Additionally, we endeavor to determine translational impact in healthcare, accelerating effective treatments, enhancement of clinical decision support systems, and promotion of health equity. Statistical methods include but not limited to machine learning methods for ophthalmic imaging data, finite sample correction, time-to-event analysis, regression models for complex dependencies, etc.