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Using Observational Data to Calibrate Simulation Models

Author

Listed:
  • Eleanor J. Murray
  • James M. Robins
  • George R. Seage III
  • Sara Lodi
  • Emily P. Hyle
  • Krishna P. Reddy
  • Kenneth A. Freedberg
  • Miguel A. Hernán

Abstract

Background. Individual-level simulation models are valuable tools for comparing the impact of clinical or public health interventions on population health and cost outcomes over time. However, a key challenge is ensuring that outcome estimates correctly reflect real-world impacts. Calibration to targets obtained from randomized trials may be insufficient if trials do not exist for populations, time periods, or interventions of interest. Observational data can provide a wider range of calibration targets but requires methods to adjust for treatment-confounder feedback. We propose the use of the parametric g-formula to estimate calibration targets and present a case-study to demonstrate its application. Methods. We used the parametric g-formula applied to data from the HIV-CAUSAL Collaboration to estimate calibration targets for 7-y risks of AIDS and/or death (AIDS/death), as defined by the Center for Disease Control and Prevention under 3 treatment initiation strategies. We compared these targets to projections from the Cost-effectiveness of Preventing AIDS Complications (CEPAC) model for treatment-naïve individuals presenting to care in the following year ranges: 1996 to 1999, 2000 to 2002, or 2003 onwards. Results. The parametric g-formula estimated a decreased risk of AIDS/death over time and with earlier treatment. The uncalibrated CEPAC model successfully reproduced targets obtained via the g-formula for baseline 1996 to 1999, but over-estimated calibration targets in contemporary populations and failed to reproduce time trends in AIDS/death risk. Calibration to g-formula targets improved CEPAC model fit for contemporary populations. Conclusion. Individual-level simulation models are developed based on best available information about disease processes in one or more populations of interest, but these processes can change over time or between populations. The parametric g-formula provides a method for using observational data to obtain valid calibration targets and enables updating of simulation model inputs when randomized trials are not available.

Suggested Citation

  • Eleanor J. Murray & James M. Robins & George R. Seage III & Sara Lodi & Emily P. Hyle & Krishna P. Reddy & Kenneth A. Freedberg & Miguel A. Hernán, 2018. "Using Observational Data to Calibrate Simulation Models," Medical Decision Making, , vol. 38(2), pages 212-224, February.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:2:p:212-224
    DOI: 10.1177/0272989X17738753
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    References listed on IDEAS

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    1. Jakob Grazzini, 2011. "Consistent Estimation of Agent Based Models," LABORatorio R. Revelli Working Papers Series 110, LABORatorio R. Revelli, Centre for Employment Studies.
    2. 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.
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    Cited by:

    1. 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.
    2. André Moser & Milo A. Puhan & Marcel Zwahlen, 2020. "The role of causal inference in health services research II: a framework for causal inference," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 65(3), pages 367-370, April.

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