A Lung Cancer Policy Model

肺癌政策模型

• 批准号: 6729559
• 负责人: G SCOTT GAZELLE
• 金额: $ 33.97万
• 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 2007-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6729559
• 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）：拟议研究的目标是开发一种肺癌模型，能够在正在进行的临床试验结果获得之前指导未来几年的筛查政策决策。通过对选定目标人群中的大量个体进行蒙特卡罗模拟，该模型将提供螺旋 CT 和胸部 X 射线成像等拟议筛查技术的健康和经济后果的估计。 该模型的大部分价值在于将短期试验结果转化为终生健康和经济后果。来自不同来源的数据，包括过去和正在进行的筛选试验，将被整合以得出可靠的参数估计。潜在的自然历史组件将模拟癌症和良性病变的产生和生长，其参数源自现有的筛查试验数据、尸检研究和肿瘤生长率信息。每个人发生病变的概率将由吸烟史、年龄和性别等危险因素决定。其他模型组件将模拟肺癌的检测和治疗，并将结果根据患病率筛查和肿瘤登记处的现有数据进行校准。通过明确地模拟肿瘤生长和检测，我们的方法不同于以前的肺癌筛查决策分析。 该研究的主要目标是确定拟议和假设的筛查策略的成本效益，检查筛查不同目标人群或采用改进技术进行筛查的经济和健康影响，并对正在进行的试验中未包含的进行比较（例如，螺旋CT筛查与不筛查，或以不同频率进行筛查）。该模型还将允许识别阈值性能特征，高于该阈值性能特征，新的筛查计划将成为不筛查或螺旋 CT 筛查的有吸引力的替代方案。通过识别具有较大合理范围和对结果有重要影响的参数，该模型可以作为未来临床试验有效设计的工具。该模型将指导即将到来的筛查政策决策，并且与冠心病政策模型的成就并行，有一天可以评估治疗进展或发病率趋势。