Email Address: firstname.lastname@example.org
Other Research Theme Collaborations: PREM
Evidence synthesis and evaluation of health policies.Health policies, such as the introduction of new treatments, are commonly evaluated using decision-analytic models. These models combine all relevant evidence from multiple sources, commonly extrapolate it to the long term, and involve a lot of uncertain assumptions. Chris's work involves statistical methods to ensure that the decisions based on these models accurately reflect the available evidence. The extent and sources of uncertainty are also quantified to ensure that priorities for future research are set appropriately. A Bayesian framework is typically used, which allows direct data, indirect data and expert belief to be combined. Chris has been appointed to the editorial board of the journal Medical Decision Making for the 2018-2020 period.
Multi-state models for longitudinal dataMulti-state and time-to-event models in health care and disease progression. Chris maintain the msm R package for continuous-time Markov and hidden Markov modelling.
Statistical computingDeveloping and maintaining several R packages, including msm, flexsurv for survival analysis, denstrip for illustrating distributions, ecoreg for ecological inference. Contributing to the OpenBUGS and BRugs software for Bayesian analysis.
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
Jackson, C. H., Sharples, L. D. & Thompson, S. G. (2010)Survival models in health economic evaluations: balancing fit and parsimony to improve prediction.
International Journal of Biostatistics 6: (1), Article 34
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
H Thom, C Jackson, N Welton, L Sharples (2017)Using Parameter Constraints to Choose State Structures in Cost-Effectiveness Modelling
Pharmacoeconomics 35: (9): 951-962
C Jackson (2016)flexsurv: a platform for parametric survival modelling in R
Journal of Statistical Software 70: