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Incorporating Calibrated Model Parameters into Sensitivity Analyses

Author

Listed:
  • Douglas Taylor
  • Vivek Pawar
  • Denise Kruzikas
  • Kristen Gilmore
  • Myrlene Sanon
  • Milton Weinstein

Abstract

Objective: The aim of this study was to examine how calibration uncertainty affects the overall uncertainty of a mathematical model and to evaluate potential drivers of calibration uncertainty. Methods: A lifetime Markov model of the natural history of human papillomavirus (HPV) infection and cervical disease was developed to assess the cost effectiveness of a hypothetical HPV vaccine. Published data on cervical cancer incidence and mortality and prevalence of pre-cursor lesions were used as endpoints to calibrate the age- and HPV-type-specific transition probabilities between health states using the Nelder-Mead simplex method of calibration. A conventional probabilistic sensitivity analysis (PSA) was performed to assess uncertainty in vaccine efficacy, cost and utility estimates. To quantify the uncertainty around calibrated transition probabilities, a second PSA (calibration PSA) was performed using 25 distinct combinations of objective functions and starting simplexes. Results: The initial calibration produced an incremental cost-effectiveness ratio (ICER) of $US4300 per QALY for vaccination compared with no vaccination, and the conventional PSA gave a 95% credible interval of dominant to $US9800 around this estimate (2005 values). The 95% credible interval for the ICERs in the calibration PSA ranged from $US1000 to $US37 700. Conclusions: Compared with a conventional PSA, the calibration PSA results reveal a greater level of uncertainty in cost-effectiveness results. Sensitivity analyses around model calibration should be performed to account for uncertainty arising from the calibration process. Copyright Springer International Publishing AG 2012

Suggested Citation

  • Douglas Taylor & Vivek Pawar & Denise Kruzikas & Kristen Gilmore & Myrlene Sanon & Milton Weinstein, 2012. "Incorporating Calibrated Model Parameters into Sensitivity Analyses," PharmacoEconomics, Springer, vol. 30(2), pages 119-126, February.
    • Handle: RePEc:spr:pharme:v:30:y:2012:i:2:p:119-126
    DOI: 10.2165/11593360-000000000-00000
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    References listed on IDEAS

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    1. Jonathan Karnon & Carolyn Czoski-Murray & Kevin J. Smith & Christopher Brand, 2009. "A Hybrid Cohort Individual Sampling Natural History Model of Age-Related Macular Degeneration: Assessing the Cost-Effectiveness of Screening Using Probabilistic Calibration," Medical Decision Making, , vol. 29(3), pages 304-316, May.
    2. Natasha Stout & Amy Knudsen & Chung Kong & Pamela McMahon & G. Gazelle, 2009. "Calibration Methods Used in Cancer Simulation Models and Suggested Reporting Guidelines," PharmacoEconomics, Springer, vol. 27(7), pages 533-545, July.
    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. Rutter, Carolyn M. & Miglioretti, Diana L. & Savarino, James E., 2009. "Bayesian Calibration of Microsimulation Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1338-1350.
    5. Douglas Taylor & Vivek Pawar & Denise Kruzikas & Kristen Gilmore & Ankur Pandya & Rowan Iskandar & Milton Weinstein, 2010. "Methods of Model Calibration," PharmacoEconomics, Springer, vol. 28(11), pages 995-1000, November.
    6. Jonathan Karnon & Tazio Vanni, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 51-62, January.
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    1. Jing Voon Chen & Julia L. Higle & Michael Hintlian, 2018. "A systematic approach for examining the impact of calibration uncertainty in disease modeling," Computational Management Science, Springer, vol. 15(3), pages 541-561, October.

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