STATISTICAL INFERENCE FOR COST-EFFECTIVENESS ANALYSIS

成本效益分析的统计推断

• 批准号: 6199084
• 负责人: Joseph C Gardiner
• 金额: $ 26.21万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-07-01 至 2003-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6199084
• 关键词: attention deficit disorder; behavioral /social science research tag; clinical research; comorbidity; computer simulation; cost effectiveness; data collection methodology /evaluation; health care cost /financing; health care policy; health care quality; health care service evaluation; health services research tag; hospital utilization; human data; human morbidity; human therapy evaluation; implantable defibrillators; longitudinal human study; mathematical model; model design /development; mother /infant health care; myocardial infarction; neoplasm /cancer diagnosis; outcomes research; statistics /biometry

Motivated by the need for new techniques for analyses of health care costs from clinical and epidemiological studies, the goal of this study is to develop statistical techniques that fill methodological gaps in current CEA models. When a health care intervention is deployed, cost are engendered though the use of resources. These occur in random amounts at random times that might differ by patient attributes, clinical and intervention characteristics. Study design and sampling may lead to incomplete observation of key outcomes in some patients. Incorporating these components into statistical models that accurately reflect the experience of patients as their health histories manifest over time permits consideration of health outcomes and costs jointly. This study proposes statistical models of costs and patient outcomes that incorporate the temporal dynamics that generate these data and the availability of concomitant exogenous information. They propose to use a Markovian regime to model the dynamics of movement of patients through different health states, with costs incurred at transitions between states and sojourns within states. Applying a constant discount rate where appropriate, they will estimate summary health outcome measures (e.g. life expectancy, quality-adjusted life years, net present value, net health cost and benefit, cost-effectiveness ratios). They will assess the impact of exogenous factors on these parameters and provide a unified framework for statistical inference and then test the performance and sensitivity of the procedures with real and simulated data. They propose applications of the methods to several peer-reviewed investigations. 1) They will assess the determinants of total health care costs and the use and cost of pharmacologic treatments in a longitudinal study of quality and continuity of care to 9,000 Medicaid recipients with a diagnosis of attention deficit hyperactivity disorder. 2) They will estimate cost of hospitalization in relation to co-morbidity, patient demographic, and clinical attributes in a retrospective study of cardiac procedures performed in two cohorts of patients with myocardial infarction. 3) From the MADIT they will assess resource utilization and and survival in patients at high risk for ventricular arrhythmia, and re-examine the cost-effectiveness of the defibrillator compared to conventional therapy. 4) They will assess (relative to usual care) incremental costs, net health benefit and net cost measures in a trial of an intervention designed to improve mental health and physical health functioning and life course development in low-income pregnant women. 5) They will assess the determinants of cost by providing treatment in a study of stage of diagnosis, treatment, and survival in patients with cancer in Medicaid In summary, by developing and testing new methods for cost-effectiveness studies, and demonstrating their application in several ongoing clinical studies, this study not only offers an array of promising techniques, but also bridges the gap between methodological development and implementation.

出于对健康分析新技术的需求 临床和流行病学研究的护理费用，本研究的目标 是开发统计技术来填补当前方法论的空白 CEA 模型。 当部署医疗保健干预措施时，就会产生成本 虽然利用资源。 这些在随机时间以随机数量发生 可能因患者属性、临床和干预措施而异 特征。 研究设计和抽样可能导致观察不完整 一些患者的关键结果。 将这些组件合并到 准确反映患者体验的统计模型 随着时间的推移显示的健康史允许考虑健康结果 并共同承担费用。 本研究提出了成本和成本的统计模型 患者结果结合了产生这些结果的时间动态 数据和伴随的外源信息的可用性。 他们提议 使用马尔可夫机制来模拟患者运动的动态 通过不同的健康状态，在不同的健康状态之间过渡时会产生费用 州和州内的逗留。 应用恒定贴现率，其中 在适当的情况下，他们将估计总结的健康结果指标（例如生命 预期寿命、质量调整生命年、净现值、净健康成本 以及效益、成本效益比）。 他们将评估影响 外生因素对这些参数的影响，并提供一个统一的框架 统计推断，然后测试性能和灵敏度 具有真实和模拟数据的程序。 他们提出了将这些方法应用于几个同行评审的方法 调查。 1) 他们将评估总体医疗保健的决定因素 纵向研究中的成本以及药物治疗的使用和成本 为 9,000 名医疗补助受益人提供护理质量和连续性 注意力缺陷多动障碍的诊断。 2）他们会估计 住院费用与合并症、患者人口统计和 心脏手术回顾性研究中的临床特征 在两组心肌梗塞患者中。 3）从MADIT他们 将评估资源利用率和高危患者的生存率 室性心律失常，并重新审视该方案的成本效益 除颤器与​​传统治疗相比。 4）他们将评估（相对 到常规护理）增量成本、净健康效益和净成本措施 旨在改善心理健康和身体健康的干预试验 低收入孕妇的健康功能和生命历程发展。 5) 他们将通过在研究中提供治疗来评估成本的决定因素 癌症患者的诊断、治疗和生存阶段 医疗补助 总之，通过开发和测试具有成本效益的新方法 研究，并展示其在几个正在进行的临床中的应用 研究，这项研究不仅提供了一系列有前途的技术，而且 弥合了方法开发和实施之间的差距。