Multiparameter calibration of a natural history model of cervical cancer

Jane J. Kim, Karen M. Kuntz, Natasha K. Stout, Salaheddin Mahmud, Luisa L. Villa, Eduardo L. Franco, Sue J. Goldie

Research output: Contribution to journalArticlepeer-review

118 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)137-150
Number of pages14
JournalAmerican journal of epidemiology
Volume166
Issue number2
DOIs
StatePublished - Jun 2007

Keywords

  • Calibration
  • Computer simulation
  • Human papillomavirus 16
  • Human papillomavirus 18
  • Natural history
  • Papillomavirus vaccines
  • Uterine cervical neoplasms

Fingerprint Dive into the research topics of 'Multiparameter calibration of a natural history model of cervical cancer'. Together they form a unique fingerprint.

Cite this