Research services
Edward De Vol
Our team focuses on diagnostic problems where conventional methods fall short—when reference standards are imperfect, populations are non-representative, or disease processes evolve over time. By combining rigorous statistical methodology with large-scale real-world data, we enable more accurate, population-relevant, and clinically deployable diagnostics across laboratory medicine, imaging, and clinical research.
Overview
Our team applies a range of cutting-edge technologies such as in-vivo modelling, gene-editing, multi-omics, and organoid technology to investigate male infertility. We aim to improve assisted reproduction treatments and develop molecular diagnostics and therapeutics for male infertility, while also establishing new techniques for the preservation and restoration of male fertility.
Biostatistical Research for Improved Diagnostic Clinical Care
Our work addresses core limitations in contemporary diagnostics. Many tests rely on thresholds derived from external populations or gold standards that are invasive, infrequently observed, or error-prone. These constraints introduce misclassification, obscure disease dynamics, and limit confidence in diagnostic decision-making.
We develop and apply indirect estimation methods, latent-variable models, and longitudinal frameworks to quantify diagnostic performance under real-world conditions. A major program modernizes laboratory reference intervals for the Saudi population using routine clinical data, replacing imported ranges with statistically validated, locally appropriate thresholds aligned with Vision 2030 health priorities.
In parallel, we lead methodological research in areas such as liver fibrosis and other complex diseases, where diagnostics must be evaluated without perfect reference standards and across repeated measurements. Across all projects, emphasis is placed on translating analytics into implementable tools—LIS-ready thresholds, validated algorithms, and system-level diagnostic guidance.
Our Team
We are committed to methodological rigor, transparency, and collaboration, working closely with clinicians, laboratorians, and research leaders to ensure scientific credibility and real-world impact.
Edward De Vol
Principal Scientist / Team Leader
Samia Mohamed Alhashim
Research Associate
Abdulrahman bin Muammar
Research Associate
Projects
Our mission is to develop and apply advanced statistical methods to improve how diagnostic data are quantified, interpreted, and translated into clinical practice.
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