Kim Jane J, Kuntz Karen M, Stout Natasha K, Mahmud Salaheddin, Villa Luisa L, Franco Eduardo L, Goldie Sue J
Program in Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA 02115, USA.
Am J Epidemiol. 2007 Jul 15;166(2):137-50. doi: 10.1093/aje/kwm086. Epub 2007 May 25.
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.
本研究的目的是采用两步法进行模型校准，建立一个全面的人乳头瘤病毒（HPV）与宫颈癌自然史模型。第一步，作者利用巴西女性纵向研究的原始流行病学数据，确定每个输入参数的合理范围，以使模型输出处于数据的95%置信区间内。第二步，他们对所有输入参数进行同步搜索，以识别产生与多源数据一致输出的参数集。使用基于似然性的方法为555,000个独特的参数集计算拟合优度得分，并在模型中使用一组拟合良好的参数样本，通过预测一系列与宫颈癌预防政策相关的益处来说明校准方法的优势。校准后的模型在高危型HPV感染的持续时间和患病率、癌前病变的患病率以及癌症发病率方面与数据具有合理的拟合度。作者发现，利用纵向研究的原始数据为HPV感染不可观察的性质及其在宫颈癌发展中的作用进行模型参数化提供了独特的机会。
