A Lung Cancer Policy Model

肺癌政策模型

• 批准号: 7021412
• 负责人: G SCOTT GAZELLE
• 金额: $ 32.31万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-03-01 至 2008-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7021412
• 关键词: cancer registry /resource; cancer risk; computed axial tomography; cost effectiveness; diagnosis service; health care cost /financing; health care model; health care policy; health care service evaluation; health services research tag; human data; human mortality; longevity; lung neoplasms; mass screening; model design /development; neoplasm /cancer epidemiology; neoplasm /cancer radiodiagnosis; neoplastic growth; postmortem; radiography; thoracic radiography

DESCRIPTION (provided by applicant): The goal of the proposed research is to develop a lung cancer model capable of guiding screening policy decisions over the next several years, before results of ongoing clinical trials are available. Through Monte Carlo simulations of large numbers of individuals in selected target populations, the model will provide estimates of the health and economic consequences of proposed screening technologies such as helical CT and chest X-ray imaging. Much of the model's value will lie in translating short-term trial results into lifetime health and economic consequences. Data from a diverse array of sources, including past and ongoing screening trials, will be integrated to derive robust parameter estimates. An underlying natural history component will simulate the generation and growth of cancers and benign lesions, with parameters derived from existing screening trial data, autopsy studies, and information on tumor growth rates. Each individual's probability of developing lesions will be determined by risk factors such as smoking history, age, and sex. Additional model components will simulate the detection and treatment of lung cancer, with outcomes calibrated to existing data from prevalence screens and tumor registries. By modeling tumor growth and detection explicitly, our approach differs from previous decision analyses of lung cancer screening. Principal goals of the research are to determine the cost-effectiveness of proposed and hypothetical screening strategies, to examine the economic and health effects of screening different target populations or of screening with improved technologies, and to make comparisons not included in ongoing trials (e.g., helical CT screening vs. no screening, or screening at different frequencies). The model will also allow identification of threshold performance characteristics, above which new screening programs would be attractive alternatives to no screening or to screening with helical CT. By identifying parameters with both large plausible ranges and important effects on results, the model can serve as a tool for use in the efficient design of future clinical trials. The model will guide imminent screening policy decisions, and, paralleling achievements of the coronary heart disease policy model, could one day evaluate treatment advances or incidence trends.

描述（由申请人提供）：拟议研究的目的是开发一个肺癌模型，能够在未来几年内指导筛查政策决策，然后才能进行正在进行的临床试验结果。通过对选定目标人群中大量个体的蒙特卡洛模拟，该模型将估算拟议筛查技术（例如螺旋CT和胸部X射线成像）的健康和经济后果。 该模型的大部分价值将在于将短期试验结果转化为终生健康和经济后果。来自各种来源的数据，包括过去和正在进行的筛选试验，将集成以得出可靠的参数估计。潜在的自然历史成分将模拟癌症和良性病变的产生和生长，并从现有的筛查试验数据，尸检研究以及有关肿瘤生长速率的信息中得出参数。每个人患病变的可能性将取决于诸如吸烟病史，年龄和性别等危险因素。其他模型组件将模拟肺癌的检测和治疗，并根据患病率筛查和肿瘤注册表的现有数据进行校准。通过明确对肿瘤生长和检测进行建模，我们的方法与以前的肺癌筛查的决策分析不同。 该研究的主要目标是确定拟议和假设筛选策略的成本效益，以检查筛查不同目标人群或通过改进的技术进行筛选的经济和健康效应，并进行对比的比较不包括在正在进行的试验中（例如，螺旋的CT筛选vs.不筛选或在不同频率上进行筛选）。该模型还将允许识别阈值性能特征，在上面，新的筛选程序将是无筛查或使用螺旋CT进行筛选的有吸引力的替代方案。通过识别具有较大合理范围的参数和对结果的重要影响，该模型可以用作有效设计未来临床试验的工具。该模型将指导迫在眉睫的筛查政策决策，以及与冠心病政策模型的并行成就，有一天可以评估治疗的进步或发病率趋势。