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Evidence synthesis, parameter correlation and probabilistic sensitivity analysis

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  • A. E. Ades
  • Karl Claxton
  • Mark Sculpher

Abstract

Over the last decade or so, there have been many developments in methods to handle uncertainty in cost‐effectiveness studies. In decision modelling, it is widely accepted that there needs to be an assessment of how sensitive the decision is to uncertainty in parameter values. The rationale for probabilistic sensitivity analysis (PSA) is primarily based on a consideration of the needs of decision makers in assessing the consequences of decision uncertainty. In this paper, we highlight some further compelling reasons for adopting probabilistic methods for decision modelling and sensitivity analysis, and specifically for adopting simulation from a Bayesian posterior distribution. Our reasoning is as follows. Firstly, cost‐effectiveness analyses need to be based on all the available evidence, not a selected subset, and the uncertainties in the data need to be propagated through the model in order to provide a correct analysis of the uncertainties in the decision. In many – perhaps most – cases the evidence structure requires a statistical analysis that inevitably induces correlations between parameters. Deterministic sensitivity analysis requires that models are run with parameters fixed at ‘extreme’ values, but where parameter correlation exists it is not possible to identify sets of parameter values that can be considered ‘extreme’ in a meaningful sense. However, a correct probabilistic analysis can be readily achieved by Monte Carlo sampling from the joint posterior distribution of parameters. In this paper, we review some evidence structures commonly occurring in decision models, where analyses that correctly reflect the uncertainty in the data induce correlations between parameters. Frequently, this is because the evidence base includes information on functions of several parameters. It follows that, if health technology assessments are to be based on a correct analysis of all available data, then probabilistic methods must be used both for sensitivity analysis and for estimation of expected costs and benefits. Copyright © 2005 John Wiley & Sons, Ltd.

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  • A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
    • Handle: RePEc:wly:hlthec:v:15:y:2006:i:4:p:373-381
    DOI: 10.1002/hec.1068
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    as
    1. David M. Eddy, 1989. "The Confidence Profile Method: A Bayesian Method for Assessing Health Technologies," Operations Research, INFORMS, vol. 37(2), pages 210-228, April.
    2. Joanne Lord & Maxwell A. Asante, 1999. "Estimating uncertainty ranges for costs by the bootstrap procedure combined with probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(4), pages 323-333, June.
    3. A. E. Ades & S. Cliffe, 2002. "Markov Chain Monte Carlo Estimation of a Multiparameter Decision Model: Consistency of Evidence and the Accurate Assessment of Uncertainty," Medical Decision Making, , vol. 22(4), pages 359-371, August.
    4. James C. Felli & Gordon B. Hazen, 1999. "A Bayesian approach to sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 263-268, May.
    5. Aaron A. Stinnett & John Mullahy, 1998. "Net Health Benefits," Medical Decision Making, , vol. 18(2_suppl), pages 68-80, April.
    6. Robert T. Clemen & Robert L. Winkler, 1985. "Limits for the Precision and Value of Information from Dependent Sources," Operations Research, INFORMS, vol. 33(2), pages 427-442, April.
    7. Kevin P. Brand & Mitchell J. Small, 1995. "Updating Uncertainty in an Integrated Risk Assessment: Conceptual Framework and Methods," Risk Analysis, John Wiley & Sons, vol. 15(6), pages 719-729, December.
    8. Anand Patwardhan & Mitchell J. Small, 1992. "Bayesian Methods for Model Uncertainty Analysis with Application to Future Sea Level Rise," Risk Analysis, John Wiley & Sons, vol. 12(4), pages 513-523, December.
    9. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
    10. Andrew H. Briggs & A. E. Ades & Martin J. Price, 2003. "Probabilistic Sensitivity Analysis for Decision Trees with Multiple Branches: Use of the Dirichlet Distribution in a Bayesian Framework," Medical Decision Making, , vol. 23(4), pages 341-350, July.
    11. N. J. Welton & A. E. Ades, 2005. "A model of toxoplasmosis incidence in the UK: evidence synthesis and consistency of evidence," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(2), pages 385-404, April.
    12. Aaron A. Stinnett & John Mullahy, 1998. "Net Health Benefits: A New Framework for the Analysis of Uncertainty in Cost-Effectiveness Analysis," NBER Technical Working Papers 0227, National Bureau of Economic Research, Inc.
    13. Kimberly M. Thompson & David E. Burmaster & Edmund A.C. Crouch3, 1992. "Monte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 12(1), pages 53-63, March.
    14. Vic Hasselblad & Douglas C. McCrory, 1995. "Meta-analytic Tools for Medical Decision Making: A Practical Guide," Medical Decision Making, , vol. 15(1), pages 81-96, February.
    15. Andrew E. Smith & P. Barry Ryan & John S. Evans, 1992. "The Effect of Neglecting Correlations When Propagating Uncertainty and Estimating the Population Distribution of Risk," Risk Analysis, John Wiley & Sons, vol. 12(4), pages 467-474, December.
    16. Gregory C. Critchfield & Keith E. Willard, 1986. "Probabilistic Analysis of Decision Trees Using Monte Carlo Simulation," Medical Decision Making, , vol. 6(2), pages 85-92, June.
    17. Giovanni Parmigiani & Greg P. Samsa & Marek Ancukiewicz & Joseph Lipscomb & Vic Hasselblad & David B. Matchar, 1997. "Assessing Uncertainty in Cost-Effectiveness Analyses," Medical Decision Making, , vol. 17(4), pages 390-401, October.
    18. Antonio G. Chessa & Rommert Dekker & Bas Van Vliet & Ewout W. Steyerberg & J. Dik F. Habbema, 1999. "Correlations in Uncertainty Analysis for Medical Decision Making," Medical Decision Making, , vol. 19(3), pages 276-286, August.
    19. Peter Doubilet & Colin B. Begg & Milton C. Weinstein & Peter Braun & Barbara J. McNeil, 1985. "Probabilistic Sensitivity Analysis Using Monte Carlo Simulation," Medical Decision Making, , vol. 5(2), pages 157-177, June.
    20. Maxine E. Dakins & John E. Toll & Mitchell J. Small & Kevin P. Brand, 1996. "Risk‐Based Environmental Remediation: Bayesian Monte Carlo Analysis and the Expected Value of Sample Information," Risk Analysis, John Wiley & Sons, vol. 16(1), pages 67-79, February.
    21. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
    22. Kimberly M. Thompson, 2002. "Variability and Uncertainty Meet Risk Management and Risk Communication," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 647-654, June.
    23. A. E. Ades & G. Lu, 2003. "Correlations Between Parameters in Risk Models: Estimation and Propagation of Uncertainty by Markov Chain Monte Carlo," Risk Analysis, John Wiley & Sons, vol. 23(6), pages 1165-1172, December.
    24. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261, May.
    25. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
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    2. Jackson Christopher H & Sharples Linda D & Thompson Simon G, 2010. "Survival Models in Health Economic Evaluations: Balancing Fit and Parsimony to Improve Prediction," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-31, October.
    3. Tazio Vanni & Jonathan Karnon & Jason Madan & Richard White & W. Edmunds & Anna Foss & Rosa Legood, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 35-49, January.
    4. Christopher H. Jackson & Mark Jit & Linda D. Sharples & Daniela De Angelis, 2015. "Calibration of Complex Models through Bayesian Evidence Synthesis," Medical Decision Making, , vol. 35(2), pages 148-161, February.
    5. Hawre Jalal & Jeremy D. Goldhaber-Fiebert & Karen M. Kuntz, 2015. "Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling," Medical Decision Making, , vol. 35(5), pages 584-595, July.
    6. Sun-Young Kim & Louise B. Russell & Anushua Sinha, 2015. "Handling Parameter Uncertainty in Cost-Effectiveness Models Simply and Responsibly," Medical Decision Making, , vol. 35(5), pages 567-569, July.
    7. C. Armero & G. García‐Donato & A. López‐Quílez, 2010. "Bayesian methods in cost–effectiveness studies: objectivity, computation and other relevant aspects," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 629-643, June.
    8. Testa, Riccardo & Foderà, Mario & Di Trapani, Anna Maria & Tudisca, Salvatore & Sgroi, Filippo, 2016. "Giant reed as energy crop for Southern Italy: An economic feasibility study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 558-564.
    9. Simon Walker & Mark Sculpher & Karl Claxton & Steve Palmer, 2012. "Coverage with evidence development, only in research, risk sharing or patient access scheme? A framework for coverage decisions," Working Papers 077cherp, Centre for Health Economics, University of York.
    10. Benjamin Thorpe & Orlagh Carroll & Linda Sharples, 2018. "Methods for Handling Unobserved Covariates in a Bayesian Update of a Cost-effectiveness Model," Medical Decision Making, , vol. 38(2), pages 150-162, February.
    11. Zafar Zafari & Kristian Thorlund & J. FitzGerald & Carlo Marra & Mohsen Sadatsafavi, 2014. "Network vs. Pairwise Meta-Analyses: A Case Study of the Impact of an Evidence-Synthesis Paradigm on Value of Information Outcomes," PharmacoEconomics, Springer, vol. 32(10), pages 995-1004, October.
    12. Denis Getsios & Kristen Migliaccio-Walle & Jaime Caro, 2007. "NICE Cost-Effectiveness Appraisal of Cholinesterase Inhibitors," PharmacoEconomics, Springer, vol. 25(12), pages 997-1006, December.

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