HIERARCHICAL STATISTICAL MODELING IN HEALTH POLICY RESEA

卫生政策研究中的层次统计模型

• 批准号: 2235945
• 负责人: CARL N MORRIS
• 金额: $ 35.32万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-08-01 至 1996-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2235945
• 关键词: HIERARCHICAL; STATISTICAL; MODELING; HEALTH; POLICY

The research proposed in this application is designed to capitalize on and further advance two exciting new developments in statistics, hierarchical (multi-level) statistical modeling, and meta-analysis, in order to address key issues in medical effectiveness, in health services, and in health policy research. The scope of problems addressed by these methods will include: modeling and quantifying variation in utilization rates and outcomes across study units (e.g., geographic areas, service areas, hospitals, or health care providers); and formulating improved random effects and meta-analysis approaches to combine diagnostic and therapeutic procedures. Hierarchical models may be used to provide better inferences for individual units by borrowing strength of information from the ensemble. They make it possible to account properly for different levels and sources of variation in complex datasets, and they often enable analyses of questions that cannot even be formulated by standard methods. This proposal brings together a network of statistical and health policy researchers from several Harvard departments, and two national advisory groups, one composed of leading statisticians in hierarchical models, meta- analysis, probability networks and health services research, and the other composed of senior subject-matter researchers from several PORT's. This research team will accomplish the transfer of new statistical technology to health services, outcomes and policy research, it will develop new statistical methodologies and software as needed, and it will disseminate its findings to the general community of researchers in these areas via a series of prototype analyses for specific problems.

本申请中提出的研究旨在利用和 进一步推进统计学两个令人兴奋的新发展， （多层次）统计建模和荟萃分析，以解决 医疗效果、卫生服务和健康方面的关键问题 政策研究。 这些方法解决的问题范围将 包括：建立利用率变化的模型并加以量化， 跨研究单元的结果（例如，地理区域、服务区域、 医院或卫生保健提供者）;并制定改进的随机 联合收割机诊断和治疗的效果和荟萃分析方法 程序. 分层模型可用于为个体提供更好的推断。 通过借用来自集合的信息的强度， 他们使它 能够正确解释不同的变异水平和来源 在复杂的数据集中，它们通常可以分析问题， 甚至不能用标准方法来制定。 这项建议汇集了一个统计和卫生政策网络， 来自哈佛几个系的研究人员和两个国家顾问 组，一个由层次模型中的领先统计学家组成，Meta， 分析，概率网络和卫生服务研究，以及其他 由来自多个港口的高级主题研究人员组成。 这 研究小组将完成新的统计技术的转让， 卫生服务，成果和政策研究，它将开发新的 统计方法和软件，并将分发 它的研究结果，在这些领域的研究人员通过一个 针对具体问题的一系列原型分析。