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A Computationally Efficient Method for Probabilistic Parameter Threshold Analysis for Health Economic Evaluations

Author

Listed:
  • Zoë Pieters

    (I-BioStat, Data Science Institute, Hasselt University, Hasselt, Limburg, Belgium
    Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Wilrijk, Antwerp, Belgium)

  • Mark Strong

    (School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK)

  • Virginia E. Pitzer

    (Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, CT, USA)

  • Philippe Beutels

    (Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Wilrijk, Antwerp, Belgium)

  • Joke Bilcke

    (Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Wilrijk, Antwerp, Belgium)

Abstract

Background . Threshold analysis is used to determine the threshold value of an input parameter at which a health care strategy becomes cost-effective. Typically, it is performed in a deterministic manner, in which inputs are varied one at a time while the remaining inputs are each fixed at their mean value. This approach will result in incorrect threshold values if the cost-effectiveness model is nonlinear or if inputs are correlated. Objective . To propose a probabilistic method for performing threshold analysis, which accounts for the joint uncertainty in all input parameters and makes no assumption about the linearity of the cost-effectiveness model. Methods . Three methods are compared: 1) deterministic threshold analysis (DTA); 2) a 2-level Monte Carlo approach, which is considered the gold standard; and 3) a regression-based method using a generalized additive model (GAM), which identifies threshold values directly from a probabilistic sensitivity analysis sample. Results . We applied the 3 methods to estimate the minimum probability of hospitalization for typhoid fever at which 3 different vaccination strategies become cost-effective in Uganda. The threshold probability of hospitalization at which routine vaccination at 9 months with catchup campaign to 5 years becomes cost-effective is estimated to be 0.060 and 0.061 (95% confidence interval [CI], 0.058–0.064), respectively, for 2-level and GAM. According to DTA, routine vaccination at 9 months with catchup campaign to 5 years would never become cost-effective. The threshold probability at which routine vaccination at 9 months with catchup campaign to 15 years becomes cost-effective is estimated to be 0.092 (DTA), 0.074 (2-level), and 0.072 (95% CI, 0.069–0.075) (GAM). GAM is 430 times faster than the 2-level approach. Conclusions . When the cost-effectiveness model is nonlinear, GAM provides similar threshold values to the 2-level Monte Carlo approach and is computationally more efficient. DTA provides incorrect results and should not be used.

Suggested Citation

  • Zoë Pieters & Mark Strong & Virginia E. Pitzer & Philippe Beutels & Joke Bilcke, 2020. "A Computationally Efficient Method for Probabilistic Parameter Threshold Analysis for Health Economic Evaluations," Medical Decision Making, , vol. 40(5), pages 669-679, July.
    • Handle: RePEc:sae:medema:v:40:y:2020:i:5:p:669-679
    DOI: 10.1177/0272989X20937253
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    References listed on IDEAS

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    1. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    2. Andrew Briggs, 1995. "Handling Uncertainty in the Results of Economic Evaluation," Briefing 000410, Office of Health Economics.
    3. Drummond, Michael F. & Sculpher, Mark J. & Claxton, Karl & Stoddart, Greg L. & Torrance, George W., 2015. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 4, number 9780199665884.
    4. 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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    1. Xiao Li & Joke Bilcke & Alike W. van der Velden & Robin Bruyndonckx & Samuel Coenen & Emily Bongard & Muirrean de Paor & Slawomir Chlabicz & Maciek Godycki-Cwirko & Nick Francis & Rune Aabenhus & Hein, 2023. "Cost-effectiveness of adding oseltamivir to primary care for influenza-like-illness: economic evaluation alongside the randomised controlled ALIC4E trial in 15 European countries," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(6), pages 909-922, August.

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