Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
Cancer. 2012 Feb 15;118(4):1100-9. doi: 10.1002/cncr.27405. Epub 2012 Jan 6.
The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases.
Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age-specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay-adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted.
The differences between the spatiotemporal model-estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method.
The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts.
本研究旨在评估美国癌症协会（American Cancer Society）用于预测新癌症病例数量的时空预测模型。
评估了自 2007 年以来一直在使用的模型的适应性。建模分三个步骤进行。在步骤 I 中，使用时空变化的生态预测因子来估算全国每个县的特定年龄发病率计数，即使在特定年份数据缺失的地区也能提供估计值。步骤 II 调整了步骤 I 的估计值以反映报告延迟。在步骤 III 中，将延迟调整后的预测值向前推进 4 年至当前日历年度。原始模型的适应性调整包括更新协变量和评估替代预测方法。进行了残差分析和 5 种时间预测方法的评估。
时空模型估计的病例数与 2007 年观察到的病例数之间的差异<1%。考虑到病例报告的延迟后，女性的差异为 2.5%，男性的差异为 3.3%。残差分析表明，没有明显的模式表明需要额外的协变量。向量自回归模型被确定为最佳的时间预测方法。
当前的时空预测模型足以提供合理的病例计数估计值。为了将估计的病例数向前推进 4 年，建议使用向量自回归模型作为最接近观察到的病例数的最佳时间预测方法。
