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Methods of Model Calibration

Author

Listed:
  • Douglas Taylor
  • Vivek Pawar
  • Denise Kruzikas
  • Kristen Gilmore
  • Ankur Pandya
  • Rowan Iskandar
  • Milton Weinstein

Abstract

Background: Mathematical models are commonly used to predict future benefits of new therapies or interventions in the healthcare setting. The reliability of model results is greatly dependent on accuracy of model inputs but on occasion, data sources may not provide all the required inputs. Therefore, calibration of model inputs to epidemiological endpoints informed by existing data can be a useful tool to ensure credibility of the results. Objective: To compare different computational methods of calibrating a Markov model to US data. Methods: We developed a Markov model that simulates the natural history of human papillomavirus (HPV) infection and subsequent cervical disease in the US. Because the model consists of numerous transition probabilities that cannot be directly estimated from data, calibration to multiple disease endpoints was required to ensure its predictive validity. Goodness of fit was measured as the mean percentage deviation of model-predicted endpoints from target estimates. During the calibration process we used the manual, random and Nelder-Mead calibration methods. Results: The Nelder-Mead and manual calibration methods achieved the best fit, with mean deviations of 7% and 10%, respectively. Nelder-Mead accomplished this result with substantially less analyst time than the manual method, but required more intensive computing capability. The random search method achieved a mean deviation of 39%, which we considered unacceptable despite the ease of implementation of that method. Conclusions: The Nelder-Mead and manual techniques may be preferable calibration methods based on both performance and efficiency, provided that sufficient resources are available. Copyright Adis Data Information BV 2010

Suggested Citation

  • 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.
  • Handle: RePEc:spr:pharme:v:28:y:2010:i:11:p:995-1000
    DOI: 10.2165/11538660-000000000-00000
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    Cited by:

    1. Xiuxian Wang & Na Geng & Jianxin Qiu & Zhibin Jiang & Liping Zhou, 2020. "Markov model and meta-heuristics combined method for cost-effectiveness analysis," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 213-235, March.
    2. C Marijn Hazelbag & Jonathan Dushoff & Emanuel M Dominic & Zinhle E Mthombothi & Wim Delva, 2020. "Calibration of individual-based models to epidemiological data: A systematic review," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-17, May.
    3. 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.

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