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CSM statistician Peter Dutton has developed a software package that optimises the design of single-arm clinical trials in rare diseases.

Head and shoulders photograph of Peter Dutton alongside four example graph outputs from the developed software. — The newly developed software helps to choose the number of participants that a trial needs to have a good chance of finding out whether the drug under investigation works (green) or is too toxic to use (red) without needing more participants (yellow).

Methods for the development and validation of computable phenotypes based on biobanks and linked real world data

Deadline - Friday 15 May 2026

Measuring the mathematical similarity and distance between real-world databases and their transformation into Common Data Models: an OMOP case study to strengthen data quality in observational research

Artificial Intelligence for musculoskeletal global health

People Powered Research – Investigating ethical and operational hurdles in public-research engagement

Assessing the use of treatments for blood cancers using real world data

Fibrosis across musculoskeletal tissues: Comparison of resolving and persistent fibrotic niches

Pro-resolving therapies: A new therapeutic paradigm targeting inflammation in equine tendon injury

Advanced Analysis of 15-Year Outcomes in the Arterial Revascularization Trial: Implications for CABG Strategies

Modelling recovery after common hand surgery procedures

Emulating and extending randomised controlled trials using routinely-collected data from Europe

Large language models for phenotyping in Real World Evidence distributed analytics: bridging expert knowledge and artificial intelligence

Improving the quality of real world evidence by measuring and minimising outcome misclassification using the OMOP common data model and large multinational health data

Osteogenic-angiogenic coupling in fracture repair - Why do some patients fail to heal?

Investigating pro-inflammatory vs pro-remission mechanisms across immune disorders: integrating single-cell/spatial multiomics & ML/AI approaches for patient-level translational insights

Investigating pro-inflammatory vs pro-remission mechanisms across immune disorders: integrating single-cell/spatial multiomics & ML/AI approaches for patient-level translational insights

Multi-dimensional analysis of the immunological synapse in the context of immunotherapy

Virtual Immune Cells – Combing Mechanism, Genomics & AI to Predict T cell responses

Multi-dimensional analysis of the immunological synapse in the context of immunotherapy

Multi-dimensional analysis of the immunological synapse in the context of immunotherapy

Integrated Ultrasound-Enhanced Biophotonic Platform for High-Throughput Studies of Cancer–Immune Cell Mechanobiology

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