Decision Analytic Cost Effectiveness Modelling

Since ScHARR´s inception in 1994 health economists and mathematical modellers have been working together to produce cost effectiveness analyses for local and national health care decision making bodies. Alongside the large volume of applied work, members of HEDS have produced a steady stream of papers on the role of modelling, the methods of modelling and quality assurance in modelling.

Good Practice in Cost Effectiveness Modelling

In 1999, ScHARR hosted an international consensus conference on good practice in cost effectiveness decision modelling. The discussion papers and consensus statement from this conference were subsequently published in the journal PharmacoEconomics in May 2000. (Used with permission of copyright holder.) The following papers are available for download:

  • Testing the Validity of Cost-Effectiveness Models [download].
  • Decision Analytic Modelling in the Economic Evaluation of Health Technologies [download].

The International Society for PharmacoEconomics and Outcomes Research (ISPOR) Task Force on Good Practice in Cost Effectiveness Modelling produced a guideline that was published in Value in Health in 2003 and can be downloaded from the links opposite (used with permission).

Analysis of uncertainty in Cost Effectiveness Modelling

Since the establishment of CHEBS, a joint research centre between ScHARR and the Department of Probability and Statistics at the University of Sheffield, HEDS has been increasingly active in research on the analysis of uncertainty in cost effectiveness modelling. Recent relevant publications are listed below.

Profs. John Brazier and Ron Akehurst were members of the Working Party that produced the most recent NICE Guide to the Methods of Technology Appraisal – a document which mandated probabilistic sensitivity analysis in cost effectiveness analyses submitted to the NICE Appraisal Programme.

Relevant publications:

  • O'Hagan, A., Stevenson, M. and Madan, J. (2005). Monte Carlo probabilistic sensitivity analysis for patient level simulation models. Research Report No. 561/05, Department of Probability and Statistics, University of Sheffield. Submitted to Health Economics.
  • Brennan, A. and Kharroubi, S. A. (2005). Efficient Computation of Partial Expected Value of Sample Information Using Bayesian Approximation. Research Report No. 560/05, Department of Probability and Statistics, University of Sheffield. Submitted to Journal of Health Economics. 
  • Brennan, A., Kharroubi, S. A., Chilcott, J. and O'Hagan, A. (2005). A two level Monte Carlo approach to calculating expected value of perfect information:- Resolution of the uncertainty in methods. Submitted to Medical Decision Making.
  • Claxton K. Sculpher M. McCabe C. Briggs A. Buxton M. Brazier J. Akehurst R. O’Hagan A. Probabilistic sensitivity analysis for NICE Technology Assessment: Not an Optional Extra. Health Economics 2005;14(4):339-348
  • O´Hagan A, McCabe C, Akehurst R, Brennan A, Briggs A, Claxton K, Fenwick E, Fryback D, Sculpher M, Spiegelhalter D, and Willan A. (2005). Incorporation of uncertainty in health economic modelling studies. PharmacoEconomics 23 (6): 529-536
  • Tappenden P, Chilcott JB, Eggington S, Oakley J, McCabe C, Methods for expected value of information analysis in complex health economic models: development on the health economic of interferon-beta and glatiramer acetate for multiple sclerosis. Health Technology Assessment 2004;8(27)