Advancing Statistical Literacy in Eye Care: A Series for Enhanced Clinical Decision-Making

This project seeks to elevate the statistical proficiency of eye care professionals to enhance clinical decision-making and research integrity within the field of ophthalmology. The inaugural part of the series, titled "Introduction to Statistical Tools for Eye Care Research," is authored by Daniela Oehring from the Faculty of Health at the University of Plymouth, UK and Pedro Miguel Serra from Ophthalmology Clinic Vista Sanchez Trancon, Spain. The study addresses the critical need for robust statistical understanding to evaluate and conduct high-quality research, thereby reducing research waste and improving evidence-based practices in eye and vision care.
Employing a dual methodological approach, the project conducts a comprehensive narrative literature review to identify key statistical concepts, common methodological flaws, and best practices specific to eye care research. Additionally, it generates simulated clinical datasets using Python to provide practical examples and visual illustrations of these statistical tools. These datasets encompass variables such as pupil diameter, refractive error, and central corneal thickness with intraocular pressure, mirroring realistic clinical scenarios.
All simulated datasets and related materials are made available through the OSF to ensure transparency, reproducibility, and accessibility for researchers and clinicians. By bridging theoretical knowledge with practical application, this project aims to foster a deeper understanding of statistical methods, ultimately leading to more reliable research outcomes and improved patient care in the realm of eye health.