Comparative Modeling of Lung Cancer Control Policies

描述(由申请人提供)：复杂的建模技术可以成为决策者寻求了解癌症控制干预措施对癌症发病率和死亡率的人群趋势的影响的强大工具。然而，由于对复杂模型缺乏透明度以及不同群体发表的结果存在差异的合理担忧，此类模型在卫生政策中已被证明的价值有限。NCI的癌症干预和监测建模网络(CISNET)是为了促进调查类似问题的独立建模组织之间的合作而创建的。通过使用相同的数据来源作为输入，并就统一的结果衡量标准达成一致，结果的可变性反映了癌症控制干预措施效果的不确定性，而不是分析设计的差异。此外，通过合作，建模小组可以连贯地解释差异的原因。 这项建议通过使用比较模型方法来估计烟草控制和筛查对减少肺癌死亡的贡献，从而进一步实现了国际烟草控制和筛查网络的目标。在接下来的5年里，主要的试验将报道螺旋CT对肺癌筛查的效果。这项建议中描述的五个CISNet模型代表了一个现有的基础设施，用来将这些新数据与来自观察性研究和队列研究以及肿瘤登记的现有数据进行合成。这组模型准备将试验结果转化为人口水平的影响，并预测如果单独或联合采用这些政策干预措施，肺癌发病率和死亡率的趋势。我们建议传播我们的结果，使决策者能够优先选择一种或多种特定的干预措施，以实现最大限度地减少肺癌死亡。

DESCRIPTION (provided by applicant): Sophisticated modeling techniques can be powerful tools for decision makers seeking to understand the effects of cancer control interventions on population trends in cancer incidence and mortality. Yet the proven value of such models in health policy is limited by legitimate concerns over lack of transparency of complex models and variability in published results from different groups. NCI's Cancer Intervention and Surveillance Modeling Network (CISNET) was created to promote collaboration between independent modeling groups investigating similar questions. By using the same sources of data for inputs and agreeing on uniform outcome measures, the variability in results reflects uncertainty in the effects of cancer control interventions rather than differences in design of the analysis. Further, by working together, the modeling groups can coherently explain the causes of variation. This proposal furthers the goals of CISNET by using comparative modeling approach to estimate the contributions of tobacco control and screening to reducing deaths from lung cancer. Over the next 5 years, major trials will report results on the efficacy of helical CT screening for lung cancer. The five CISNET models described in this proposal represent an existing infrastructure with which to synthesize these new data with existing data from observational and cohort studies and tumor registries. This group of models is poised to translate trial results into population-level effects and to project trends in lung cancer incidence and mortality if these policy interventions were adopted alone or in combination. We propose to disseminate our results in ways that will allow decision makers to prioritize one or more specific interventions to achieve the greatest reduction in lung cancer deaths.