Health economics

Health economics is concerned with assessing the cost-effectiveness of medical technologies. Computer models are often used to make cost-effectiveness estimates, and my main interest is in analysing uncertainty in cost-effectiveness model predictions. Other interests include eliciting utilities for different states of health, in particular the design and analysis of discrete choice surveys. I collaborate with researchers in ScHARR through the Centre for Bayesian Statistics in Health Economics.

Relevant publications

  • Ren, S., Oakley, J. E. and Stevens, J. W. (2018). Incorporating genuine prior information about between-study heterogeneity in random effects pairwise and network meta-analyses. Medical Decision Making, 38 (4), 531-542
  • Strong M., Oakley J. E., Brennan A. and Breeze, P. (2015). Estimating the expected value of sample information using the probabilistic sensitivity analysis sample: a fast nonparametric regression-based method. Medical Decision Making, 35(5), 570-83.
  • Strong, M. and Oakley, J. E. (2014). When is a model good enough? Deriving the expected value of model improvement via specifying internal model discrepancies. SIAM/ASA Journal on Uncertainty Quantification, 2(1), 106-125.
  • Strong M, Oakley J. E., Brennan A. (2014). Estimating multi-parameter partial Expected Value of Perfect Information from a probabilistic sensitivity analysis sample: a non-parametric regression approach. Medical Decision Making, 34(3), 311-26.
  • Strong, M. and Oakley, J. E. (2013). An efficient method for computing single parameter partial expected value of perfect information. Medical Decision Making, 33, 755-766.
  • Strong, M., Oakley J. E. and Chilcott, J. (2012). Managing structural uncertainty in health economic decision models: a discrepancy approach. Journal of the Royal Statistical Society, Series C, 61(1), 25-45
  • Strong, M. and Oakley J. E. (2011). Bayesian inference for comorbid disease risks using marginal disease risks and correlation information from a separate source. Medical Decision Making 31(4), 571-581.
  • Oakley, J.E., Brennan, A., Tappenden, P. and Chilcott, J.B. (2010). Sample sizes for Monte Carlo partial EVPI calculations. Journal of Health Economics 29(3), 468-77. Download example R code.
  • Stevenson, M. D., Oakley, J. E., Lloyd Jones, M. Brennan, A., Compston, J. E. , McCloskey E. V. and Selby P. L. (2009). The Cost-Effectiveness of an RCT to Establish Whether 5 or 10 Years of Bisphosphonate Treatment Is the Better Duration for Women With a Prior Fracture. Medical Decision Making 29(6), 678-689.
  • Stevenson, M. D., Oakley, J. E., Chick, S. E. and Chalkidou, K. (2009). The cost-effectiveness of surgical instrument management policies to reduce the risk of vCJD transmission to humans. Journal of the Operational Research Society 60, 506-518.
  • Coyle D. and Oakley J. (2008) Estimating the expected value of partial perfect information: a review of methods. The Eur. Journal of Health Economics 9, 251-259.
  • Karnon J., McIntosh A., Coster J., Bath P., Hutchinson A., Oakley J., Thomas N., Pratt P., Freeman-Parry L., Karsh B. T., Gandhi T. and Tappenden T. (2008). Modelling the expected net benefits of interventions to reduce the burden of medication errors. Journal of Health Services Research and Policy 13, 85-91.
  • Karnon, J., McIntosh, A., Bath, P., Dean, J., Hutchinson, A., Oakley, J., Thomas, N., Pratt, P., Freeman-Parry, L., Karsh, B., Gandhi, T. and Tappenden, P. (2007). Medication errors: a prospective hazard and improvement analysis. Safety Science 45, 523-539.
  • Stevenson, M. D., Lloyd Jones, M., De Nigris E., Brewer, N., Davis, S. and Oakley, J. (2005). A systematic review and economic evaluation of alendronate, etidronate, risedronate, raloxifene and teriparatide for the prevention and treatment of postmenopausal osteoporosis. Health Technology Assessment, Vol.9: No. 22.
  • Stevenson, M. D., Brazier, J. E., Calvert, N.W., Lloyd-Jones M., Oakley, J. and Kanis, J.A. (2005). Description of an individual patient methodology for calculating the cost-effectiveness of treatments for osteoporosis in women. Journal of the Operational Research Society, 56, 214-221.
  • Stevenson M.D., Oakley, J. and Chilcott, J.B. (2004). Gaussian process modelling in conjunction with individual patient simulation modelling: A case study describing the calculation of cost-effectiveness ratios for the treatment of osteoporosis. Medical Decision Making, 24(1), 89-100.
  • Tappenden, P., Chilcott, J. B., Eggington, S., Oakley, J. and McCabe, C. (2004). Methods for expected value of information analysis in complex health economic models: developments on the health economics of beta-inteferon and glatiramer acetate for multiple sclerosis. Health Technology Assessment, Vol. 8: No. 27.