A stochastic simulation model of U.S. breast cancer mortality trends from 1975 to 2000.
A stochastic simulation model of U.S. breast cancer mortality trends from 1975 to 2000.
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DOI：
10.1093/jncimonographs/lgj012
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发表时间：
2006-01-01
期刊：
影响因子：
--
通讯作者：
Glynn, Peter
中科院分区：
文献类型：
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作者：
Plevritis, Sylvia K;Sigal, Bronislava M;Glynn, Peter
背景：我们提出了一个模拟模型，可以预测 1975 年至 2000 年美国乳腺癌死亡率趋势，并量化乳房 X 光检查和辅助治疗对这些趋势的影响。该模型是在癌症干预和监测网络 (CISNET) 联盟内开发的。 方法：开发蒙特卡罗模拟，通过使用 CISNET 基本案例输入来生成个体乳腺癌患者的生活史，这些基本案例输入描述了乳腺癌风险的长期趋势、乳房 X 光检查和辅助治疗的传播模式以及乳腺癌以外原因造成的死亡。该模型部分基于对乳腺癌自然史的假设，生成患者的年龄、肿瘤大小和检测时的阶段、检测方式、死亡年龄和死亡原因（乳腺癌与其他癌症）。多个出生队列的结果按照日历年的乳腺癌死亡率进行总结。结果：预测的乳腺癌死亡率遵循 1975 年至 1995 年美国乳腺癌死亡率的总体形状，但在 1995 年之后趋于平稳，而不是观察到的下降。敏感性分析显示，由于缺乏治疗效果随时间变化的数据，辅助治疗的影响可能被低估。结论：我们开发了一个模拟模型，该模型使用 CISNET 基本案例输入，密切但不准确地再现了美国乳腺癌死亡率。研究表明，乳房X线检查和辅助治疗都有助于降低美国乳腺癌死亡率。
BACKGROUND: We present a simulation model that predicts U.S. breast cancer mortality trends from 1975 to 2000 and quantifies the impact of screening mammography and adjuvant therapy on these trends. This model was developed within the Cancer Intervention and Surveillance Network (CISNET) consortium.METHOD: A Monte Carlo simulation is developed to generate the life history of individual breast cancer patients by using CISNET base case inputs that describe the secular trend in breast cancer risk, dissemination patterns for screening mammography and adjuvant treatment, and death from causes other than breast cancer. The model generates the patient's age, tumor size and stage at detection, mode of detection, age at death, and cause of death (breast cancer versus other) based in part on assumptions on the natural history of breast cancer. Outcomes from multiple birth cohorts are summarized in terms of breast cancer mortality rates by calendar year.RESULT: Predicted breast cancer mortality rates follow the general shape of U.S. breast cancer mortality rates from 1975 to 1995 but level off after 1995 as opposed to following an observed decline. Sensitivity analysis revealed that the impact adjuvant treatment may be underestimated given the lack of data on temporal variation in treatment efficacy.CONCLUSION: We developed a simulation model that uses CISNET base case inputs and closely, but not exactly, reproduces U.S. breast cancer mortality rates. Screening mammography and adjuvant therapy are shown to have both contributed to a decline in U.S. breast cancer mortality.