Rosenberg Philip S, Check David P, Anderson William F
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, NIH, Bethesda, Maryland.
Cancer Epidemiol Biomarkers Prev. 2014 Nov;23(11):2296-302. doi: 10.1158/1055-9965.EPI-14-0300. Epub 2014 Aug 21.
Age-period-cohort (APC) analysis can inform registry-based studies of cancer incidence and mortality, but concerns about statistical identifiability and interpretability, as well as the learning curves of statistical software packages, have limited its uptake.
We implemented a panel of easy-to-interpret estimable APC functions and corresponding Wald tests in R code that can be accessed through a user-friendly Web tool.
Input data for the Web tool consist of age-specific numbers of events and person-years over time, in the form of a rate matrix of paired columns. Output functions include model-based estimators of cross-sectional and longitudinal age-specific rates, period and cohort rate ratios that incorporate the overall annual percentage change (net drift), and estimators of the age-specific annual percentage change (local drifts). The Web tool includes built-in examples for teaching and demonstration. User data can be input from a Microsoft Excel worksheet or by uploading a comma-separated-value file. Model outputs can be saved in a variety of formats, including R and Excel.
APC methodology can now be carried out through a freely available user-friendly Web tool. The tool can be accessed at http://analysistools.nci.nih.gov/apc/.
The Web tool can help cancer surveillance researchers make important discoveries about emerging cancer trends and patterns.
年龄-时期-队列(APC)分析可为基于登记处的癌症发病率和死亡率研究提供信息,但对统计可识别性和可解释性的担忧,以及统计软件包的学习曲线,限制了其应用。
我们在R代码中实现了一组易于解释的可估计APC函数和相应的Wald检验,可通过一个用户友好的网络工具访问。
网络工具的输入数据由随时间变化的特定年龄事件数和人年数组成,形式为成对列的率矩阵。输出函数包括基于模型的横断面和纵向特定年龄率估计值、纳入总体年度百分比变化(净漂移)的时期和队列率比,以及特定年龄年度百分比变化(局部漂移)的估计值。网络工具包括用于教学和演示的内置示例。用户数据可以从Microsoft Excel工作表输入,也可以通过上传逗号分隔值文件输入。模型输出可以保存为多种格式,包括R和Excel格式。
现在可以通过一个免费的用户友好网络工具进行APC方法。该工具可在http://analysistools.nci.nih.gov/apc/访问。
该网络工具可帮助癌症监测研究人员发现有关新兴癌症趋势和模式的重要信息。