Predicting county-level cancer incidence rates and counts in the USA.
Predicting county-level cancer incidence rates and counts in the USA.
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DOI：
10.1002/sim.5833
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发表时间：
2013-09-30
影响因子：
2
通讯作者：
Yu B
中科院分区：
文献类型：
--
作者：
Yu B
包括美国在内的许多国家都公布了当前和未来几年全国癌症发病率和死亡率的预测数字。这些预测为癌症控制规划者、政策制定者和公众提供了有关癌症负担的重要信息。根据几项实证研究的证据，美国癌症协会采用了joinpoint（分段线性回归）模型来估计自2007年以来美国和各州的新发癌症病例数。最近，较小地理区域（如县和FIPS代码区域）的癌症发病率越来越受到当地政策制定者的关注。自然的扩展是将联合点模型直接应用于县级癌症发病率数据。直接应用有几个缺点，其性能尚未得到评估。为了解决这些问题，我们开发了一个空间随机效应连接点模型的县级癌症发病率数据。该模型被用来预测癌症发病率和计数在县级。通过验证研究，标准的连接点模型和所提出的方法进行了比较。所提出的方法在几乎所有癌症部位，特别是中度或罕见癌症部位以及人口规模较小的县，都优于标准连接点模型。作为应用，我们预测了康涅狄格州2011年县级前列腺癌发病率和计数。
Many countries, including the United States, publish predicted numbers of cancer incidence and death in current and future years for the whole country. These predictions provide important information on the cancer burden for cancer control planners, policymakers and the general public. Based on evidence from several empirical studies, the joinpoint (segmented-line linear regression) model has been adopted by the American Cancer Society to estimate the number of new cancer cases in the United States and in individual states since 2007. Recently, cancer incidence in smaller geographic regions such as counties and FIPS code regions is of increasing interest by local policymakers. The natural extension is to directly apply the joinpoint model to county-level cancer incidence data. The direct application has several drawbacks and its performance has not been evaluated. To address the concerns, we developed a spatial random-effects joinpoint model for county-level cancer incidence data. The proposed model was used to predict both cancer incidence rates and counts at the county level. The standard joinpoint model and the proposed method were compared through a validation study. The proposed method out-performed the standard joinpoint model for almost all cancer sites, especially for moderate or rare cancer sites and for counties with small population sizes. As an application, we predicted county-level prostate cancer incidence rates and counts for the year 2011 in Connecticut.
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影响因子：
254.7
作者：
Jemal, Ahmedin;Siegel, Rebecca;Thun, Michael J.
通讯作者：
Thun, Michael J.
半参数贝叶斯的方法是连接基于人群的癌症生存数据的重点回归。
影响因子：
1.8
作者：
Ghosh P;Huang L;Yu B;Tiwari RC
通讯作者：
Tiwari RC
影响因子：
2.1
作者：
Clements, MS;Armstrong, BK;Moolgavkar, SH
通讯作者：
Moolgavkar, SH
影响因子：
254.7
作者：
Tiwari, RC;Ghosh, K;Feuer, EJ
通讯作者：
Feuer, EJ
影响因子：
10.3
作者：
Mariotto, Angela B.;Yabroff, K. Robin;Brown, Martin L.
通讯作者：
Brown, Martin L.