Lung cancer rate predictions using generalized additive models
Lung cancer rate predictions using generalized additive models
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DOI：
10.1093/biostatistics/kxi028
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发表时间：
2005-10-01
期刊：
影响因子：
2.1
通讯作者：
Moolgavkar, SH
中科院分区：
文献类型：
--
作者：
Clements, MS;Armstrong, BK;Moolgavkar, SH
肺癌发病率和死亡率的预测是必要的规划公共卫生项目和临床服务。广义加性模型（GAMs）是一种实用的癌症预测模型。经典年龄-时期、年龄-队列和年龄-时期-队列模型的平滑等价物可以使用一维平滑样条。我们还建议使用二维平滑样条的年龄和时期。方差估计可以基于自助法。为了评估预测性能，我们将这些模型与贝叶斯年龄-时期-队列模型进行了比较。模型比较使用交叉验证和最近预测的预测性能的措施。这些模型应用于世界卫生组织死亡率数据库中五个国家的女性数据。年龄-时期-队列模型和二维模型之间的模型选择在交叉验证方面是模棱两可的，而二维GAM具有非常好的预测性能。由于预测不精确和观测数据外的线性假设，贝叶斯模型表现不佳。总之，二维GAM表现良好。GAM做出了重要的预测，即这些国家的女性肺癌发病率将保持稳定或在未来开始下降。
Predictions of lung cancer incidence and mortality are necessary for planning public health programs and clinical services. It is proposed that generalized additive models (GAMs) are practical for cancer rate prediction. Smooth equivalents for classical age-period, age-cohort, and age-period-cohort models are available using one-dimensional smoothing splines. We also propose using two-dimensional smoothing splines for age and period. Variance estimation can be based on the bootstrap. To assess predictive performance, we compared the models with a Bayesian age-period-cohort model. Model comparison used cross-validation and measures of predictive performance for recent predictions. The models were applied to data from the World Health Organization Mortality Database for females in five countries. Model choice between the age-period-cohort models and the two-dimensional models was equivocal with respect to cross-validation, while the two-dimensional GAMs had very good predictive performance. The Bayesian model performed poorly due to imprecise predictions and the assumption of linearity outside of observed data. In summary, the two-dimensional GAM performed well. The GAMs make the important prediction that female lung cancer rates in these countries will be stable or begin to decline in the future.