Rate My Professor Samuel Watson

Professor Samuel Watson is Professor of Biostatistics in the Department of Applied Health Sciences at the University of Birmingham, where he serves as a statistician in the Institute of Applied Health Research. He holds a PhD in Health Sciences from the University of Warwick (2014), an MSc in Health Economics from City University of London (2011), and a BSc (Hons) in Natural Sciences from the University of Bath (2010). His research centres on applied statistics in public health and healthcare, with a focus on experimental design, particularly cluster randomised trials, spatial statistics, and their intersections. Watson employs methods for evaluation in low-resource settings, including cluster trial and survey methodologies using Bayesian approaches. He leads several major projects, such as the NIHR-funded Strategies for the efficient design of cluster randomised trials to evaluate novel interventions for infectious disease control (2025-2028), Optimising the delivery of Diabetes Distress informed care (D-Stress study, 2024-2029), and Evidence on quality and cost of care for women funded by the Bill & Melinda Gates Foundation (2025-2027). As co-investigator, he contributes to initiatives like the NIHR Global Health Research Unit on Improving Health in Slums, the NIHR RIGHT programme on leprosy and Buruli ulcer, and stepped-wedge trials integrating mental health care in Malawi and remote consulting in Tanzania and Nigeria.

Watson's scholarly output includes over 60 articles, with key publications such as 'Covariate adjustment in cluster randomised trials: a practical guide' (BMJ, 2025, with Karla Hemming et al.), 'Design and analysis of randomized trials to estimate spatio-temporally heterogeneous treatment effects' (Journal of the American Statistical Association, 2025), 'A randomised controlled trial of raw honey for the healing of ulcers in leprosy in Nigeria' (PLoS Neglected Tropical Diseases, 2025), 'Protocol for a review of statistical methods used to estimate risk ratios and risk differences in parallel cluster randomised trials' (Trials, 2026), and earlier works like 'Estimating the effect of health service delivery interventions on patient length of stay: a Bayesian survival analysis approach' (Journal of the Royal Statistical Society Series C, 2021) and 'Design and analysis of three-arm parallel cluster randomized trials with small numbers of clusters' (Statistics in Medicine, 2021). His contributions advance methods for cluster trials, geospatial analysis, and real-time disease surveillance, influencing global health research in low- and middle-income countries. He accepts PhD students in applied statistics focusing on experimental design, cluster trials, and spatial statistics.