Background: The American Cancer Society (ACS) and the NCI collaborate every 5 to 8 years to update the methods for estimating the numbers of new cancer cases and deaths in the current year for the U.S. and individual states. Herein, we compare our current projection methodology with the next generation of statistical models. Methods: A validation study was conducted comparing current projection methods (vector autoregression for incidence; Joinpoint regression for mortality) with the Bayes state-space method and novel Joinpoint algorithms. Incidence data from 1996–2010 were projected to 2014 using two inputs: modeled data and observed data with modeled where observed were missing. For mortality, observed data from 1995 to 2009, 1996 to 2010, 1997 to 2011, and 1998 to 2012, each projected 3 years forward to 2012 to 2015. Projection methods were evaluated using the average absolute relative deviation (AARD) between observed counts (2014 for incidence, 2012–2015 for mortality) and estimates for 47 cancer sites nationally and 21 sites by state. Results: A novel Joinpoint model provided a good fit for both incidence and mortality, particularly for the most common cancers in the U.S. Notably, the AARD for cancers with cases in 2014 exceeding 49,000 for this model was 3.4%, nearly half that of the current method (6.3%). Conclusions: A data-driven Joinpoint algorithm had versatile performance at the national and state levels and will replace the ACS's current methods. Impact: This methodology provides estimates of cancer data that are not available for the current year, thus continuing to fill an important gap for advocacy, research, and public health planning.

中文翻译：

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用于预测当前日历年美国和州级癌症计数的更新方法：第二部分：发病率和死亡率预测方法的评估

背景：美国癌症协会 (ACS) 和 NCI 每 5 到 8 年合作一次，更新估计美国和个别州当年新癌症病例和死亡人数的方法。在这里，我们将我们当前的预测方法与下一代统计模型进行比较。方法：进行了一项验证研究，将当前的投影方法（发病率的向量自回归；死亡率的 Joinpoint 回归）与贝叶斯状态空间方法和新的 Joinpoint 算法进行比较。1996 年至 2010 年的发病率数据使用两个输入预测到 2014 年：建模数据和观察到的数据，在观察到缺失的地方建模。对于死亡率，1995 年至 2009 年、1996 年至 2010 年、1997 年至 2011 年和 1998 年至 2012 年的观察数据均预测了 3 年至 2012 年至 2015 年。使用观察计数（2014 年发病率，2012-2015 年死亡率）与全国 47 个癌症部位和各州 21 个部位的估计值之间的平均绝对相对偏差 (AARD) 评估预测方法。结果：新的 Joinpoint 模型为发病率和死亡率提供了很好的拟合，特别是对于美国最常见的癌症 值得注意的是，该模型在 2014 年病例超过 49,000 的癌症的 AARD 为 3.4%，几乎是当前的一半方法 (6.3%)。结论：数据驱动的 Joinpoint 算法在国家和州级具有通用性能，将取代 ACS 的当前方法。影响：该方法提供了当年无法获得的癌症数据估计值，从而继续填补宣传、研究和公共卫生规划的重要空白。