Multiparameter calibration of a natural history model of cervical cancer.
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.
Kim
Jane J
JJ
Program in Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA 02115, USA. jkim@hsph.harvard.edu
Kuntz
Karen M
KM
Stout
Natasha K
NK
Mahmud
Salaheddin
S
Villa
Luisa L
LL
Franco
Eduardo L
EL
Goldie
Sue J
SJ
D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D001938:Brazil / Q000453:epidemiology; D002648:Child; D005260:Female; D052162:Human papillomavirus 16; D006801:Humans; D008137:Longitudinal Studies; D008875:Middle Aged; D008954:Models, Biological; D009010:Monte Carlo Method; D030361:Papillomavirus Infections / Q000150:complications / Q000453:epidemiology*; D053918:Papillomavirus Vaccines; D015995:Prevalence; D002578:Uterine Cervical Dysplasia / Q000453:epidemiology* / Q000517:prevention & control / Q000821:virology
D053918:Papillomavirus Vaccines