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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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    References listed on IDEAS

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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Denis Getsios & Kristen Migliaccio-Walle & Jaime Caro, 2007. "NICE Cost-Effectiveness Appraisal of Cholinesterase Inhibitors," PharmacoEconomics, Springer, vol. 25(12), pages 997-1006, December.
    12. 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.

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