Mark Strong BSc MB ChB MPH MSc PhD MRCPCH MFPH CStat
Reader in Public Health Decision Making
Director, Public Health Section
Dr Mark Strong
Section of Public Health
School of Health and Related Research
The University of Sheffield
30 Regent Street
Sheffield S1 4DA
Tel: 0114 222 0812
Fax: 0114 222 0749
PA: Karen Johnson firstname.lastname@example.org 0114 222 0757
*** New course on Bayesian Methods in Health Economics in Florence Italy ***
Our new residential summer school will take place in the beautiful Centro Studi CISL in Florence from 12-16 June 2017. The five-day course will provide an introduction to Bayesian methods for health economic analysis, covering both theory and computation. A programme is available here. The course will use the open source software R, BUGS and JAGS.
I am an academic public health doctor and a statistician. I am a Reader in Public Health Decision Making and the Director of the Public Health Section at ScHARR.
I welcome PhD applications at any time. You are welcome to email me to discuss an idea before making an application through the ScHARR online system. I supervise students who are interested in the use of mathematical models in health and health care. See here for a project on Value of Information that is currently advertised.
ScHARR hosts a prestigious Wellcome Trust Doctoral Training Centre in Public Health, Economics and Decision Science. Applications will open again in Autumn 2017.
Check out our web-app - SAVI - Sheffield Accelerated Value of Information
This web-app lets you calculate Expected Value of (Partial) Perfect Information for any cost-effectiveness model. Just upload the probabilistic sensitivity analysis sample and the app does the rest.
SAVI is now available as an R package from GitHub. This allows users to run the SAVI app on their own machine, and removes the need to transfer any data over the net. Installation instructions are here.
My Research interests
I have three related research interests that fall under the general banner of Uncertainty Quantification: (1) how do we properly account for all relevant uncertainties when we build a computer model of a physical, biological or social system? (2) how do we (efficiently) compute value of information? (3) how do we work out the value of a computer model? How much should we pay to make a simple model more complex? When do we stop increasing the complexity of a model?
Jeremy Oakley, Jim Chilcott and I have proposed an "internal" discrepancy-based method for managing model uncertainty. See this paper in JRSS Series C, and this paper in SIAM/ASA Journal of Uncertainty Quantification that develops the idea of the 'Expected Value of Model Improvement'. The method is discussed in more detail in my PhD thesis.
We have proposed an efficient method for computing partial EVPI. This method works for any number of parameters of interest and requires only the PSA sample. See this open access paper in Medical Decision Making. R functions to implement the method can be downloaded here. This paper uses Gaussian process-based methods that are nicely described in the Managing Uncertainty in Complex Models (MUCM) toolkit.
The partial EVPI method extends nicely to the computation of EVSI. See here for our open access paper on the efficient computation of EVSI.
I teach on the Master in Public Health and undergraduate medical MB ChB degrees, and offer MFPH exam support to the Yorkshire and Humber public health training scheme.
I am interested in hearing from students who wish to pursue masters, MPhil or PhD projects that are focused on mathematical modelling in health or healthcare.
I am an honorary consultant in public health medicine at Public Health England.
I am a member of the Editorial Board of Medical Decision Making.
Current and recent projects
- Calibrated Agent Simulations for Combined Analysis of Drinking Etiologies (CASCADE): A US National Institutes of Health funded project on alcohol consumption (2016-2021). PI - Robin Purshouse.
- Investigation of the association between alcohol outlet density and alcohol related hospital admission rates in England. Alcohol Research UK (Jan 2015 to Dec 2016). PI - Ravi Maheswaran.
- NOSH (NOurishing Start for Health): An MRC funded study exploring the potential of offering financial incentives to improve breastfeeding rates in low uptake neighbourhoods (2012-2016). PI - Clare Relton.
- Strong M, Oakley JE, Brennan A, Breeze P. Estimating the Expected Value of Sample Information using the Probabilistic Sensitivity Analysis Sample. A Fast Non-Parametric Regression Based Method. Medical Decision Making. 2015;35(5):570-583 Open Access. doi: 10.1177/0272989X15575286.
- Strong M, Oakley JE. When is a model good enough? Deriving the expected value of model improvement via specifying internal model discrepancies. SIAM/ASA Journal on Uncertainty Quantification. 2014;2(1):106-125. Open Access. DOI:10.1137/120889563
- Strong M, Oakley JE, Brennan A. Estimating multi-parameter partial Expected Value of Perfect Information from a probabilistic sensitivity analysis sample: a non-parametric regression approach. Medical Decision Making. 2014;34(3):311-26. Open Access. R code here.
- Strong M, Oakley JE, Chilcott, J. Managing structural uncertainty in health economic decision models: a discrepancy approach. Journal of the Royal Statistical Society, Series C. 2012;61(1):25-45. Preprint here.
- Strong M, Oakley JE. Bayesian inference for comorbid disease risks using marginal disease risks and correlation information from a separate source. Medical Decision Making 2011;31(4):571-581. Preprint here.