Summary
Christopher Jackson is a Senior Statistician at the BSU.
Research Overview
Health 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
Chris's work involves statistical methods to ensure that the decisions based on these kind of models accurately reflect the available evidence. For example,
- 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)
He has also made developments to Value of Information methods for quantifying uncertainties and prioritising further research in these models (paper)(review paper).
Chris has also 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 Computing
Chris develops and maintains 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
Previously contributed to the OpenBUGS and BRugs software for Bayesian analysis.
Selected Papers
Jackson, C. H., Tom, B. D., Kirwan, P. D., Mandal, S., Seaman, S. R., Kunzmann, K., Presanis, A., De Angelis, D. (2022) A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19. Statistical Methods in Medical Research 31: (9): 1656-1674
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)
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 :
