Dr Sam Brilleman is a Research Fellow in Biostatistics at the School of Public Health and Preventive Medicine.

He is a member of the Victorian Centre for Biostatistics (ViCBiostat), where he collaborates with biostatisticians from across Melbourne and globally on a variety of methodological research projects. He also provides statistical input on a range of applied health research projects being conducted within the School. He has previously worked as an applied biostatistician in several world-leading academic health research departments including the the School of Social and Community Medicine (University of Bristol), School of Population Health (University of Queensland), and Research Centre for Gender, Health and Ageing (University of Newcastle).

His PhD, awarded by Monash University in 2018, was entitled 'Joint longitudinal and time-to-event models: development, implementation and applications in health research' and was supervised by Professor Rory Wolfe, Dr Margarita Moreno-Betancur, and Dr Michael Crowther. He also holds a BSc(Hons) in Statistics from the University of Otago, NZ, and an MSc in Medical Statistics from the University of Leicester, UK.

He is a member of both the Royal Statistical Society and the Statistical Society of Australia.

Achievements and Awards

• 2018: StanCon 2018 Scholarship
• 2017: International Society for Clinical Biostatistics (ISCB) Student Conference Award
• 2015 - 2018: Australian National Health and Medical Research Council (NHMRC) Postgraduate Scholarship
• 2015 - 2017: Victorian Centre for Biostatistics (ViCBiostat) Scholarship
• 2015 - 2016: Monash University (Aus) Faculty Postgraduate Excellence Award
• 2014: UK Royal Statistical Society Prize
• 2013 - 2015: UK National Institute for Health Research (NIHR) Predoctoral Research Methods Fellowship
• 2006: Statistics New Zealand Prize
• 2006: University of Otago (NZ) Beverly Bursary in Mathematics and Statistics

Dr Brilleman has a keen interest in survival analysis, joint longitudinal-survival models, models for longitudinal data from cohort studies, Bayesian inference, and in the design of Bayesian adaptive clinical trials. He is a regular user of Stata, R and Stan, and has contributed software packages to the latter two.

• Joint modelling of longitudinal and survival data
• Longitudinal data analysis
• Survival analysis
• Bayesian inference
• Bayesian adaptive clinical trials
• R
• Stata
• Stan