Kuopio 2011 summer course

Novel quantitative methods in the evaluation of therapeutic and economic value of medicines

First MeHTA study group meeting, 9-10 June 2011, Kuopio, Finland.

This short course, jointly organized by the University of Eastern Finland and the University of Groningen, is an introduction to state of the art quantitative approaches to health policy decision making. It covers evidence synthesis, with a focus on network meta-analysis, benefit-risk decision modeling and (briefly) multi-criteria cost effectiveness analysis. In addition to the theory and examples, the course also introduces the software developed for this purpose by the University and University Medical Center of Groningen.


  1. Introducing Pharmacoeconomics and Outcomes Research Unit (PHORU) and MeHTA project (Janne Martikainen).
    Files and links
  2. Introducing the Escher 3.2 project: information technology for evidence synthesis and benefit-risk (Gert van Valkenhoef).
    Files and links
  3. Mixed treatment comparison and cost-effectiveness analysis of triptans as an example (Christian Asseburg).
    Files and links
  4. Introduction to network meta-analysis (Gert van Valkenhoef).
    Files and links
    • Caldwell, D.M., Ades, A.E. and Higgins, J.P.T., Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ, 331 (7521): 897-900, 2005. doi: 10.1136/bmj.331.7521.897
    • Cipriani, A., Furukawa, T.A., Salanti, G., Geddes, J.R., Higgins, J.P.T., Churchill, R., Watanabe, N., Nakagawa, A., Omori, I.M., McGuire, H., Tansella, M. and Barbui, C., Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis. The Lancet, 373 (9665): 746-758, 2009. doi: 10.1016/S0140-6736(09)60046-5
    • Salanti, G., Kavvoura, F.K. and Ioannidis, J.P.A., Exploring the Geometry of Treatment Networks. Annals of Internal Medicine, 148 (7): 544-553, 2008. pmid: 18378949
    • Lu, G. and Ades, A.E., Combination of Direct and Indirect Evidence in Mixed Treatment Comparisons. Statistics in Medicine 23 (20): 3105-3124, 2004. doi: 10.1002/sim.1875
  5. drugis.org software for network meta-analysis (Gert van Valkenhoef).
    Files and links
    • Lu, G. and Ades, A.E., Assessing Evidence Inconsistency in Mixed Treatment Comparisons. Journal of the American Statistical Association 101 (474): 447-459, 2006. doi: 10.1198/016214505000001302
    • Salanti, G., Higgins, J.P.T., Ades, A.E. and Ioannidis, J.P.A., Evaluation of Networks of Randomized Trials. Statistical Methods in Medical Research 17 (3): 279-301, 2008. doi: 10.1177/0962280207080643
    • Dias, S. Welton, N.J., Caldwell, D.M. and Ades, A.E., Checking Consistency in Mixed Treatment Comparison Meta-Analysis. Statistics in Medicine 29 (7-8, Sp. Iss. SI): 932-944, 2010. doi: 10.1002/sim.3767
    • Jackman, S., Bayesian Analysis for the Social Sciences. Wiley Series in Probability and Statistics, 2009. Google Books
    • Brooks, S.P. and Gelman, A., General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics 7 (4): 434--455, 1998. jstor: 1390675
  6. Benefit-risk analysis: overview of current approaches (Gert van Valkenhoef).
    Files and links
    • EMA (2010). Benefit-risk methodology project work package 2 report: Applicability of current tools and processes for regulatory benefit-risk assessment. EMA/549682/2010. EMA website
    • P. M. Coplan, R. A. Noel, B.S. Levitan, J. Ferguson and F. Mussen. Development of a Framework for Enhancing the Transparency, Reproducibility and Communication of the Benefit-Risk Balance of Medicines. Clinical pharmacology & Therapeutics 89 (2): 312-315, 2011. doi: 10.1038/clpt.2010.291
    • Walker S., McAuslane N., Liberti L. and Salek S., Measuring benefit and balancing risk: strategies for the benefit-risk assessment of new medicines in a risk-averse environment. Clinical Pharmacology & Therapeutics 85 (3): 241-246, 2009. doi: 10.1038/clpt.2008.277
    • Lynd, L. D. and O'Brien, B. J., Advances in risk-benefit evaluation using probabilistic simulation methods: an application to the prophylaxis of deep vein thrombosis. Journal of Clinical Epidemiology 57 (8): 795-803, 2004. doi: 10.1016/j.jclinepi.2003.12.012
  7. SMAA for benefit-risk analysis (Gert van Valkenhoef).
    Files and links
    • T. Tervonen, G. van Valkenhoef, E. Buskens, H.L. Hillege and D. Postmus. A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis. Statistics in Medicine 30 (12): 1419-1428, 2011. doi: 10.1002/sim.4194
    • Tervonen, T. and Figueira, J.R., A survey on stochastic multicriteria acceptability analysis methods. Journal of Multi-Criteria Decision Analysis, 15 (1-2): 1-14, 2008. doi: 10.1002/mcda.407
    • Lahdelma, R., Miettinen, K. and Salminen, P., Ordinal criteria in stochastic multicriteria acceptability analysis (SMAA). European Journal of Operational Research, 147 (1): 117-127, 2003. doi: 10.1016/S0377-2217(02)00267-9
    • Lahdelma, R. and Salminen, P., SMAA-2: Stochastic multicriteria acceptability analysis for group decision making. Operations Research, 49 (3): 444-454, 2001. doi: 10.1287/opre.49.3.444.11220
    • Mustajoki, J., Hämäläinen, R. P. and Salo, A., Decision Support by Interval SMART/SWING — Incorporating Imprecision in the SMART and SWING Methods. Decision Sciences, 36 (2): 317-339, 2005. doi: 10.1111/j.1540-5414.2005.00075.x
  8. SMAA for cost-effectiveness analysis (Gert van Valkenhoef, slides by Douwe Postmus).
    Files and links
    • M. F. Drummond, M. J. Sculpher, G. W. Torrance, B. J. O'Brien and G. L. Stoddart, Methods for the economic evaluation of health care programmes. 3rd edition, Oxford University Press, 2005. Google Books