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American Journal of Epidemiology Advance Access originally published online on May 25, 2007
American Journal of Epidemiology 2007 166(2):137-150; doi:10.1093/aje/kwm086
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American Journal of Epidemiology © The Author 2007. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

PRACTICE OF EPIDEMIOLOGY

Multiparameter Calibration of a Natural History Model of Cervical Cancer

Jane J. Kim1, Karen M. Kuntz1, Natasha K. Stout1, Salaheddin Mahmud2, Luisa L. Villa3, Eduardo L. Franco2 and Sue J. Goldie1

1 Program in Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA
2 Division of Cancer Epidemiology, McGill University, Montreal, Quebec, Canada
3 Ludwig Institute for Cancer Research, São Paulo, Brazil

Correspondence to Dr. Jane J. Kim, Program in Health Decision Science, Harvard School of Public Health, 718 Huntington Avenue, 2nd Floor, Boston, MA 02115 (e-mail: jkim{at}hsph.harvard.edu).

Received for publication September 12, 2006. Accepted for publication January 25, 2007.

The objective of this study was to develop a comprehensive natural history model of human papillomavirus (HPV) and cervical cancer using a two-step approach to model calibration. In the first step, the authors utilized primary epidemiologic data from a longitudinal study of women in Brazil and identified a plausible range for each input parameter that produced model output within the 95% confidence intervals of the data. In the second step, they performed a simultaneous search over all input parameters to identify parameter sets that produced output consistent with data from multiple sources. A goodness-of-fit score was computed for 555,000 unique parameter sets using a likelihood-based approach, and a sample of good-fitting parameter sets was used in the model to illustrate the advantage of the calibration approach by projecting a range of benefits associated with cervical cancer prevention policies. The calibrated model had reasonable fit to the data in terms of duration and prevalence of HPV infection for high-risk types, prevalence of precancerous lesions, and incidence of cancer. The authors found that leveraging primary data from longitudinal studies provides unique opportunities for model parameterization of the unobservable nature of HPV infection and its role in the development of cervical cancer.

calibration; computer simulation; human papillomavirus 16; human papillomavirus 18; natural history; papillomavirus vaccines; uterine cervical neoplasms


Abbreviations: CIN, cervical intraepithelial neoplasia; HPV, human papillomavirus; HSIL, high-grade squamous intraepithelial lesion; LSIL, low-grade squamous intraepithelial lesion


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