Kym Snell

Kym Snell is an Associate Professor in Biostatistics at Applied Health Sciences, specializing in clinical prediction modeling and healthcare data science. Their work focuses on methodological advancements for sample size calculations, model validation, and fairness in AI-driven clinical tools.

• Current roles: Researcher in projects on recurrent neck pain, diabetes remission, and cardiovascular risk prediction
• Collaborations: Medical Research Council, UK Research and Innovation, NIHR

Research Interests
Kym's research spans:

• Clinical prediction model development and validation
• Meta-analysis and systematic review methodologies
• Artificial intelligence applications in healthcare
• Biostatistical approaches for nested case-control studies

Article Trends
Recent publications emphasize sample size optimization for clinical models (Fisher information, threshold methodology) and AI integration in healthcare. Key subfields include cardiovascular risk stratification, postpartum health, electrolyte disorders, and multidomain pain prediction.

Projects
Active leadership in:

• SS-UPDATE: Sample size guidance for clinical model fairness
• REVAMP: Methodology handbook for prognostic reviews
• Multidomain Neck Pain: Data-driven recurrence prediction