Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada.
Stat Med. 2011 Dec 20;30(29):3387-402. doi: 10.1002/sim.4373. Epub 2011 Oct 3.
This paper provides a systematic comparison of cancer mortality and incidence projection methods used at major national health agencies. These methods include Poisson regression using an age-period-cohort model as well as a simple log-linear trend, a joinpoint technique, which accounts for sharp changes, autoregressive time series and state-space models. We assess and compare the reliability of these projection methods by using Canadian cancer mortality data for 12 cancer sites at both the national and regional levels. Cancer sites were chosen to provide a wide range of mortality frequencies. We explore specific techniques for small case counts and for overall national-level projections based on regional-level data. No single method is omnibus in terms of superior performance across a wide range of cancer sites and for all sizes of populations. However, the procedures based on age-period-cohort models used by the Association of the Nordic Cancer Registries tend to provide better performance than the other methods considered. The exception is when case counts are small, where the average of the observed counts over the recent 5-year period yields better predictions.
本文对主要国家卫生机构使用的癌症死亡率和发病率预测方法进行了系统比较。这些方法包括使用年龄-时期-队列模型的泊松回归以及简单的对数线性趋势、考虑急剧变化的连接点技术、自回归时间序列和状态空间模型。我们使用加拿大癌症死亡率数据，在国家和地区层面上对 12 个癌症部位进行了评估和比较这些预测方法的可靠性。选择癌症部位是为了提供广泛的死亡率频率。我们探索了针对小病例计数和基于区域水平数据的总体国家水平预测的特定技术。没有一种方法在广泛的癌症部位和所有人群规模方面都具有卓越的性能。然而，北欧癌症登记协会使用的基于年龄-时期-队列模型的程序往往比其他考虑的方法具有更好的性能。当病例数较少时，情况则有所不同，最近 5 年的观察计数平均值可产生更好的预测。
