Michael Crowther, PhD
Founder and CEO
Michael was for many years an academic biostatistician, rising to Associate Professor of Biostatistics at the University of Leicester, before relocating to Stockholm in 2021 to pursue some new challenges. He is an internationally renowned expert in survival analysis and joint longitudinal-survival models, having made numerous contributions to the fields, and widely respected as a leading statistical software developer. He has developed and taught many training courses on his research, and is a Fellow of the UK Higher Education Academy. He currently holds a 20% position as a Biostatistician at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, is an Honorary Senior Lecturer at the University of Bristol, and spends the rest of his time running Red Door Analytics.
- PhD in Medical Statistics, University of Leicester, UK, 2014 with thesis Development and application of methodology for the parametric analysis of complex survival and joint longitudinal-survival data in biomedical research
- MSc Medical Statistics, University of Leicester, UK, 2010
- MMath Mathematics and Statistics, University of St Andrews, UK, 2009
Crowther MJ, Royston P, Clements M. A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors. Biostatistics 2022; (Online First).
Crowther MJ. Simulating time-to-event data from parametric distributions, custom distributions, competing risk models and general multi-state models. The Stata Journal 2022; 22(1):3-24.
Crowther MJ, Lambert PC. Parametric multi-state survival models: flexible modelling allowing transition-specific distributions with application to estimating clinically useful measures of effect differences. Statistics in Medicine 2017;36(29):4719-4742.
Crowther MJ. merlin – a unified framework for data analysis and methods development in Stata. The Stata Journal 2020;20(4):763-784.
Crowther MJ, Andersson TM-L, Lambert PC, Abrams KR and Humphreys K. Joint modelling of longitudinal and survival data: Incorporating delayed entry and an assessment of model misspecification. Statistics in Medicine 2016;35(7)1193-1209.
Gould AL, Boye ME, Crowther MJ, Ibrahim JG, Quartey G, Micallef S, Bois FY. Joint modelling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modelling working group. Statistics in Medicine 2015;34(14):2181-2195.