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中文摘要
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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对癌症发病率和死亡率人口趋势的影响的有力工具。然而,由于对复杂模型缺乏透明度和不同群体公布结果的可变性的合理关切,这些模型在卫生政策中已证实的价值受到限制。NCI的癌症干预和监测模型网络(CISNET)的建立是为了促进研究类似问题的独立模型小组之间的合作。通过使用相同的数据来源作为输入并同意统一的结果测量,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,通过一起工作,建模组可以连贯地解释变化的原因。
英文摘要
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.
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Modeling Best Approaches for Cardiovascular Disease Prevention in Cancer Survivors
Optimizing Lung Cancer Screening in Cancer Survivors
Optimizing Lung Cancer Screening in Cancer Survivors
Optimizing Lung Cancer Screening Nodule Evaluation
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