Institute of Technology Assessment, 101 Merrimac St., Boston, MA 02114-4724, USA.
Risk Anal. 2012 Jul;32 Suppl 1(Suppl 1):S166-78. doi: 10.1111/j.1539-6924.2011.01714.x.
Sophisticated modeling techniques can be powerful tools to help us understand the effects of cancer control interventions on population trends in cancer incidence and mortality. Readers of journal articles are, however, rarely supplied with modeling details. Six modeling groups collaborated as part of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) to investigate the contribution of U.S. tobacco-control efforts toward reducing lung cancer deaths over the period 1975-2000. The six models included in this monograph were developed independently and use distinct, complementary approaches toward modeling the natural history of lung cancer. The models used the same data for inputs, and agreed on the design of the analysis and the outcome measures. This article highlights aspects of the models that are most relevant to similarities of or differences between the results. Structured comparisons can increase the transparency of these complex models.
复杂的建模技术可以成为帮助我们理解癌症控制干预措施对癌症发病率和死亡率的人口趋势的影响的有力工具。然而，期刊文章的读者很少能提供建模细节。作为美国国家癌症研究所癌症干预和监测建模网络 (CISNET) 的一部分，六个建模小组合作，研究美国烟草控制工作对降低 1975-2000 年期间肺癌死亡率的贡献。本专论中包含的六个模型是独立开发的，并且使用独特的、互补的方法来对肺癌的自然史进行建模。这些模型使用相同的数据作为输入，并就分析和结果测量的设计达成一致。本文重点介绍了与结果的相似性或差异性最相关的模型方面。结构化比较可以提高这些复杂模型的透明度。
