SURPH: Statistical methods Using data Resources to improve Population HealthTelephone number:
Email Address: email@example.com
Other Research Theme Collaborations: PREM
Evidence synthesis and evaluation of health policiesHealth policies are commonly informed using models that combine evidence from multiple sources. For example,
- models to inform policies for prevention of chronic diseases, e.g. routine mid-life health checks (paper)
- decision models used by NICE to recommend whether a new treatment should be funded by the health service
- how to specify Bayesian models that allow direct data, indirect data and expert belief to be combined (paper)
- methods to combine short-term data from clinical trials with longer-term data and assumptions ("survival extrapolation") (paper)
- methods to compare alternative model assumptions ("structural uncertainty") (paper|paper)
Survival and multistate modellingChris has developed methods and software for survival and multistate modelling, in particular parametric survival models (paper) and multi-state models for intermittently-observed data (paper). He recently developed a novel framework for multistate modelling, which was applied to hospital admissions for COVID-19 (paper).
Chris is an Associate Editor of the journal Biometrics.
Statistical computingDeveloping and maintaining several R packages, including
- msm for multistate modelling of intermittently-observed data
- flexsurv for parametric survival modelling and multistate modelling of time-to-event data
- voi for calculating the Expected Value of Information
- disbayes for estimation of chronic disease burden from indirect data
- denstrip for illustrating distributions
- fic for focused model comparison
- survextrap for Bayesian survival extrapolation
- Public short course at MRC Biostatistics Unit: Bayesian Statistics (to be advertised soon)
- University of Cambridge, MPhil in Population Health Sciences, Bayesian Statistics module
- University of Cambridge, Statistics in Mathematical Tripos Part III: coordinating and teaching on Statistics in Medical Practice
- Multi-state modelling with msm: freely-available course material.
- Armitage Lecture workshop 2021: Bayesian multi-state modelling of incomplete chronic disease data for health impact models
Google Scholar profile
Selected PapersJackson, C. H., Jit, M. D., Sharples, L. D. & De Angelis, D. (2013)Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial.
Medical Decision Making 35: (2): 148-161
Lunn, D., Jackson, C., Best, N., Thomas, A. & Spiegelhalter, D. (2012)The BUGS Book: A Practical Introduction to Bayesian Analysis.
CRC Press :
Jackson, C., Bojke, L., Thompson, S. G., Claxton, K. & Sharples, L. D. (2011)A framework for addressing structural uncertainty in decision models.
Medical Decision Making 31: (4), 662-674
Jackson, C. (2011)Multi-State Models for Panel Data: The msm Package for R.
Journal of Statistical Software 38: (8)
Jackson, C. H., Sharples, L. D. & Thompson, S. G. (2010)Structural and parameter uncertainty in Bayesian cost-effectiveness models.
Journal of the Royal Statistical Society, Series C 59: (2), 233-253
C Jackson, J Stevens, S Ren, N Latimer, L Bojke, A Manca, L Sharples (2017)Extrapolating survival from randomized trials using external data: a review of methods
Medical Decision Making 37: 377-390
C Jackson (2016)flexsurv: a platform for parametric survival modelling in R
Journal of Statistical Software 70:
Mytton, O. T., Jackson, C., Steinacher, A., Goodman, A., Langenberg, C., Griffin, S., Wareham N. & Woodcock, J. (2018)The current and potential health benefits of the National Health Service Health Check cardiovascular disease prevention programme in England: A microsimulation study
PLoS Medicine 15(3): e1002517
Jackson, C. H., Presanis, A. M., Conti, S. and De Angelis, D. (2018)Value of Information: Sensitivity Analysis and Research Prioritisation in Bayesian Evidence Synthesis
Journal of the Americal Statistical Association : (accepted for publication)