Publications

Below is a list of research publications associated with the Centre.

Research Papers

  • 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. 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.  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. R code here.
  • Turner RM, Lloyd-Jones M, Anumba DOC, Squires H, Smith GCS, Spiegelhalter DJ, Stevens JW, Sweeting MJ, Urbaniak SJ, Webster R, Thompson SG. Routine antenatal anti-D prophylaxis in women who are Rh(D) negative: meta-analyses adjusted for differences in study design and quality. PLOS ONE. 2012. https://doi.org/10.1371/journal.pone.0030711
  • Stevens JW. A note on dealing with missing standard errors in meta-analyses of continuous outcome measures in WinBUGS. Pharmaceutical Statistics. 2011;10:374–378. http://dx.doi.org/10.1002/pst.491
  • Stevens JW. Uncertainty analysis is inherently Bayesian. Value in Health. 2011;14(1):203-204 https://doi.org/10.1016/j.jval.2010.10.002
  • Ara R, Stevens JW, Ward SE, Rees A, Durrington PN, Reynolds TM, Wierzbicki A, Stevenson MD, Pandor A. Prescribing high dose lipid-lowering therapy early to avoid subsequent cardiovascular events – Is this a cost-effective strategy? European Journal of Preventive Cardiology. 2012;19(3):474-483 https://doi.org/10.1177/1741826711406616
  • Brazier JE, Fukuhara S, Roberts J, Kharoubi S et al. Estimating a preference-based index from the Japanese SF-36. Journal of Clinical Epidemiology. 2009;62(12):1323–1331 https://doi.org/10.1016/j.jclinepi.2009.01.022
  • Stevenson MD, Lloyd Jones M, Papaioannou D. Vitamin K to prevent fractures in older women. Health Technology Assessment. 2009;13(45):iii-xi:1-134. doi: 10.3310/hta13450.
  • Stevenson, MD, Oakley JE, Chick SE, Chalkidou K. The cost-effectiveness of surgical instrument management policies to reduce the risk of vCJD transmission to humans. Journal of the Operational Research Society. 2009;60:506. doi:10.1057/palgrave.jors.2602580
  • Stevenson MD, Oakley JE, Lloyd Jones M, Brennan A, Compston JE, McCloskey EV, Selby PL. 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. 2009;29(6):678-689 https://doi.org/10.1177/0272989X09336077
  • Thokala P, Goodacre SW, O’Collinson P, Stevens JW, Mills NL, Newby DE, Morris F, Kendall J, Stevenson MD. Cost-effectiveness of presentation versus delayed troponin testing for acute myocardial infarction. Heart. 2012; 98: 1498-1503
  • Strong M, Oakley JE. An efficient method for computing single-parameter partial expected value of perfect information. Medical Decision Making. E-Pub ahead of print. Dec 28, 2012, doi: 10.1177/0272989X12465123. Algorithm available here, with 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.
  • Kharroubi SA, Brennan A, Strong M. Estimating expected value of sample information for incomplete data models using Bayesian approximation. Medical Decision Making. 2011;31(6):839-852. doi: 10.1177/0272989X11399920
  • 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.
  • Whyte S, Walsh C, Chilcott J. Bayesian calibration of a natural history model with application to a population model for colorectal cancer. Medical Decision Making doi:10.1177/0272989X10384738.
  • Kharroubi SA, O'Hagan A, Brazier JE. A comparison of United States and United Kingdom EQ-5D health states valuations using a nonparametric Bayesian method. Statistics in Medicine 2010;29(15):1622-34.
  • Stevens JW. NICE work: How NICE decides what we should pay for. British Journal of General Practice 2010; 60(570):7-8.
  • Nixon RM, O'Hagan A, Oakley J, Madan J, Stevens JW, Bansback N, Brennan, A. The Rheumatoid Arthritis Drug Development Model: A case study in Bayesian clinical trial simulation. Pharmaceutical Statistics 2009;8:371-389.
  • Ara R, Pandor A, Stevens J, Rees A, Rafia R. (2009). Early high-dose lipid lowering therapy to avoid cardiac events: a systematic review and economic evaluation. Health Technology Assessment, Vol. 13: No. 34.
  • Nixon RM, Bansback N, Stevens JW, Brennan A, Madan J (2009). Using short-term evidence to predict six-month outcomes in clinical trials of signs and symptoms in rheumatoid arthritis. Pharmaceutical Statistics, 8: 150-162.
  • Oakley J (2009). Decision-theoretic sensitivity analysis for complex computer models. Technometrics, 51, 121-129.
  • McCabe C, Claxton K, O'Hagan A. (2008). Why licensing authorities need to consider the net value of new drugs in assigning review priorities: Addressing the tension between licensing and reimbursement. International Journal of Technology Assessment in Health Care 24, 140-145.
  • Brennan A, Kharroubi SA (2007). Expected value of sample information for Weibull survival data. Health Economics, 16, 1205-1225.
  • Brennan A, Kharroubi SA (2007). Efficient computation of partial expected value of sample information using Bayesian approximation. Journal of Health Economics 26, 122-148.
  • Brennan A, Kharroubi SA, O'Hagan A, Chilcott J (2007). Calculating partial expected value of information in cost-effectiveness models. Medical Decision Making 27, 448-470.
  • Kharroubi SA, Brazier JE, O'Hagan A (2007). Modelling covariates for the SF-6D standard gamble health state preference data using a nonparametric Bayesian method. Social Science and Medicine 64, 1242-1252.
  • Kharroubi SA, Brazier JE, Roberts J, O'Hagan A (2007). Modelling SF-6D health state preference data using a nonparametric Bayesian method. Journal of Health Economics 26, 597-612.
  • Oakley JE, O'Hagan A (2007). Uncertainty in prior elicitations: a nonparametric approach. Biometrika 94, 427-441.
  • O'Hagan A, Stevenson M, Madan J (2007). Monte Carlo probabilistic sensitivity analysis for patient level simulation models: : Efficient estimation of mean and variance using ANOVA. Health Economics 16, 1009-1023.
  • Ward S, Lloyd Jones M, Pandor A, Holmes M, Ara R, Ryan A, Yeo W, Payne N (2007). Health Technology Assessment Vol. 11, No. 14.
  • Connock M, Burls A, Frew E, Fry-Smith A, Juarez-García A, McCabe C, Wailoo A, Abrams K, Cooper N, Sutton A, O'Hagan A, Moore D (2006). The clinical effectiveness and cost-effectiveness of enzyme replacement therapy for Gaucher's disease: a systematic review. Health Technology Assessment Vol. 10, No. 24.
  • McCabe C, Brazier J, Gilks P, Tsuchiya A, Roberts J, O'Hagan A, Stevens K (2006). Using rank data to estimate health state utility models. Journal of Health Economics 25, 418-431.
  • Claxton K, Sculpher M, McCabe C, Briggs A, Buxton M, Brazier J, Akehurst R, O'Hagan A (2005). Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Economics 14, 339-347.
  • Kharroubi SA, O'Hagan A, Brazier JE (2005). Estimating utilities from individual health state preference data: a nonparametric Bayesian approach. Applied Statistics 54, 879-895.
  • O´Hagan A, McCabe C, Akehurst R, Brennan A, Briggs A, Claxton K, Fenwick E, Fryback D, Sculpher M, Spiegelhalter D, Willan A (2005). Incorporation of uncertainty in health economic modelling studies. PharmacoEconomics 23 (6): 529-536.
  • O'Hagan A, Stevens JW, Campbell MJ (2005). Assurance in clinical trial design. Pharmaceutical Statistics 4, 187-201.
  • O'Hagan A, Stevens JW (2004). On estimators of medical costs with censored data. Journal of Health Economics, 23, 615-625.
  • Chilcott J, Brennan A, Booth A, Karnon J, Tappenden P (2003). The role of modelling in the planning and prioritisation of clincial trials. Health Technology Assessment Vol. 7, No. 23.
  • Chilcott J, McCabe C, Tappenden P, O'Hagan A, Cooper NJ, Abrams K, Claxton K (2003). Modelling the cost effectiveness of interferon beta and glatiramer acetate in the management of multiple sclerosis. British Medical Journal 326, 522-526.
  • O'Hagan A, Stevens JW (2003) Assessing and comparing costs: How robust are the bootstrap and methods based on asymptotic normality? Health Economics 12, 33-49.
  • Stevens JW, O'Hagan A, Miller P (2003). Case study in the Bayesian analysis of a cost-effectiveness trial in the evaluation of health care technologies: Depression. Pharmaceutical Statistics, 51 68.
  • van Hout BA, Gagnon DD, McNulty P, O´Hagan A (2003). The cost-effectiveness of two new anti-epileptic therapies in the absence of direct comparative data: a first approximation. PharmacoEconomics, 21, 315-326.
  • Wight J, Chilcott J, Holmes M, Brewer N (2003). The clinical and cost-effectiveness of pulsatile machine perfusion versus cold storage of kidneys for transplantation retrieved from heart-beating and non-heart-beating donors. Health Technology Assessment Vol. 7, No. 25.
  • O'Hagan A, Stevens JW (2002). The probability of cost effectiveness. BMC Medical Research Methodology 2:5.
  • O'Hagan A, Stevens JW (2002). Bayesian methods for design and analysis of cost-effectiveness trials in the evaluation of health care technologies. Statistical Methods in Medical Research 11, 469 490.
  • Stevens JW, O'Hagan A (2002). Incorporation of genuine prior information in cost-effectiveness analysis. International Journal of Technology Assessment in Health Care 18, 782-790.
  • O'Hagan A, Stevens JW (2001). A framework for cost effectiveness analysis from clinical trial data. Health Economics 10, 302-315.
  • O'Hagan A, Stevens JW (2001). Bayesian assessment of sample size for clinical trials of cost effectiveness. Medical Decision Making 21, 219-230.
  • O'Hagan A, Stevens JW, Montmartin J (2001). Bayesian cost effectiveness analysis from clinical trial data. Statistics in Medicine 20, 733-753.
  • O'Hagan A, Stevens JW, Montmartin J (2000). Inference for the C/E Acceptability Curve and C/E Ratio. PharmacoEconomics 17, 339-349.
  • Payne N, Chilcott J, McGoogan E (2000). Liquid-based cytology in cervical screening: a rapid and systematic review. Health Technology Assessment Vol. 4, No. 18.
  • Kharroubi SA, Brennan A, Strong M. Estimating expected value of sample information for incomplete data models using Bayesian approximation. Medical Decision Making doi: 0.1177/0272989X11399920.
  • Oakley JE, Brennan A, Tappenden P, Chilcott JB (2010). Sample sizes for Monte Carlo partial EVPI calculations. Journal of Health Economics 29(3) 468-477.
  • Kharroubi SA, Brennan A (2011). A novel formulation for approximate Bayesian computation based on signed roots of log-density ratios.  Advances and Applications in Statistics 22(1) 57-76.

Books

  • Brazier J, Ratcliffe J, Tsuchiva A, Salomon J (2007). Measuring and valuing health benefits for economic valuation. OUP. 360pp. ISBN-10: 0-19-856982-3
  • O'Hagan A, Buck CE, Daneshkhah A, Eiser JR, Garthwaite PH, Jenkinson D.J, Oakley JE, Rakow T (2006). Uncertain Judgements: Eliciting Expert Probabilities. John Wiley and Sons, Chichester. 328pp. ISBN 0-470-02999-4.
  • Luce B, O'Hagan A. A Primer on Bayesian Statistics in Health Economics and Outcomes Research.