Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA.
Department of Public Health Sciences, UC Davis School of Medicine, Davis, California, USA and Group Health Research Institute, Seattle, WA, USA and Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA.
Med Decis Making. 2018 Apr;38(1_suppl):9S-23S. doi: 10.1177/0272989X17700624.
Since their inception in 2000, the Cancer Intervention and Surveillance Network (CISNET) breast cancer models have collaborated to use a nationally representative core of common input parameters to represent key components of breast cancer control in each model. Employment of common inputs permits greater ability to compare model output than when each model begins with different input parameters. The use of common inputs also enhances inferences about the results, and provides a range of reasonable results based on variations in model structure, assumptions, and methods of use of the input values. The common input data are updated for each analysis to ensure that they reflect the most current practice and knowledge about breast cancer. The common core of parameters includes population rates of births and deaths; age- and cohort-specific temporal rates of breast cancer incidence in the absence of screening and treatment; effects of risk factors on incidence trends; dissemination of plain film and digital mammography; screening test performance characteristics; stage or size distribution of screen-, interval-, and clinically- detected tumors by age; the joint distribution of ER/HER2 by age and stage; survival in the absence of screening and treatment by stage and molecular subtype; age-, stage-, and molecular subtype-specific therapy; dissemination and effectiveness of therapies over time; and competing non-breast cancer mortality.
In this paper, we summarize the methods and results for the common input values presently used in the CISNET breast cancer models, note assumptions made because of unobservable phenomena and/or unavailable data, and highlight plans for the development of future parameters.
These data are intended to enhance the transparency of the breast CISNET models.
自 2000 年成立以来,癌症干预和监测网络(CISNET)乳腺癌模型一直合作使用共同的核心输入参数来代表每个模型中乳腺癌控制的关键组成部分。使用共同的输入可以提高模型输出的可比性,而不是每个模型都从不同的输入参数开始。共同输入的使用还增强了对结果的推断,并根据模型结构、假设和输入值使用方法的变化提供了一系列合理的结果。共同输入数据在每次分析中都会更新,以确保它们反映了乳腺癌最新的实践和知识。共同的核心参数包括人口出生率和死亡率;在没有筛查和治疗的情况下,年龄和队列特定的乳腺癌发病率的时间趋势;危险因素对发病趋势的影响;普通胶片和数字乳腺摄影的传播;筛查试验性能特征;按年龄划分的筛查、间隔和临床检测肿瘤的分期或大小分布;按年龄和分期的 ER/HER2 联合分布;无筛查和治疗的生存情况按分期和分子亚型划分;年龄、分期和分子亚型特异性治疗;随时间推移的治疗传播和效果;以及竞争的非乳腺癌死亡率。
在本文中,我们总结了目前 CISNET 乳腺癌模型中共同输入值的方法和结果,注意到由于不可观察的现象和/或缺乏数据而做出的假设,并强调了未来参数的开发计划。
这些数据旨在增强乳腺癌 CISNET 模型的透明度。