Mark Clements

I am a docent and associate professor in biostatistics. I have worked as a biostatistician in Sweden, Australia, New Zealand and the United Kingdom, including positions in academia and in government. 

I am currently a member of the Management Group for the Swedish eScience Research Centre and co-leader for 

My research group includes a mix of biostatistics (x4), health economists (x3) and epidemiologists (x3).

Education

• 1991, BSc (Hons) in Mathematics and Statistics, University of Auckland
• 2001, PhD in Biostatistics and Cancer Epidemiology, University of Sydney
• 2021, Docent in Biostatistics, Karolinska Institutet.

Grants

I have received generous financial support as the principal investigator from Cancerfonden, the Swedish Research Council, Prostatacancerförbundet, ALF and KI.

Current primary PhD supervision

• Birzhan Akynkozhayev
• Xiaoyang Du
• Balram Rai

Previous primary PhD supervision

• Andreas Karlsson
• Xingrong Liu
• Shuang Hao

I have also co-supervised 13 other PhDs.

My main interests are in statistical modelling and simulation, with applications to cancer epidemiology and health economics. This includes:

• Methods for survival analysis, including generalised survival models, smooth accelerated failure time models and parametric multi-state Markov models (http://CRAN.R-project.org/package=rstpm2
• Liu et al, 2016
•  
• Liu et al, 2017
• Crowther et al, 2023).
• Modelling software for cost-effectiveness analysis (https://CRAN.R-project.org/package=microsimulation
• https://CRAN.R-project.org/package=hesim).
• Microsimulation for cost-effectiveness analysis of prostate cancer testing (https://github.com/mclements/prostata
• Karlsson et al, 2019
• Hao et al, 2021
• Hao et al, 2022
• Hao et al, 2023
• Du et al, 2024).
• Natural history modelling for cervical cancer screening (https://github.com/mclements/nkcx_analysis
• Clements, 2023).

I am also interested in computational methods in a variety of programming languages&

I am the course director for Biostatistics III: Survival analysis for epidemiologists in R, which has an accompanying R package biostat3 on CRAN and an accompanying web-site.

I will be the course director for a new masters course in