Analysis Methods for Volume-Outcome Studies

体积结果研究的分析方法

• 批准号: 6860090
• 负责人: KATHERINE S PANAGEAS
• 金额: $ 15.17万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 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/6860090
• 关键词: behavioral /social science research tag; clinical research; data collection methodology /evaluation; health care personnel performance; health care quality; health care service evaluation; health science research; health science research analysis /evaluation; health services research tag; hospital utilization; hospitals; human data; human population study; outcomes research; statistics /biometry

DESCRIPTION (provided by applicant): Numerous studies have appeared in the cancer literature in the past few years linking hospital and surgeon procedure volume with patient outcomes. Results from these studies have direct policy implications, since regionalization is a considered strategy to improve the quality and efficiency of many different types of health care. Evaluation of an association between hospital or surgeon procedure volume and patient outcomes involves complex statistical issues that arise from the fact that the unit of observation is the patient, but these studies include multiple patients per hospital or surgeon as well as multiple hospitals or surgeons. Hence, patient outcomes tend to be correlated within hospitals or within surgeons, i.e., patients treated at the same hospital or by the same surgeon, may be more likely to experience similar outcomes than patients treated by a hospital or surgeon with the same volume. This phenomenon is referred to as "clustering" of outcomes, in the presence of clustering, standard statistical methods that assume patient outcomes are independent, are invalid. The general goal of this proposal is to critically examine the validity of widely-available statistical techniques that have been used in the context of volume-outcome studies such as generalized estimating equations and random effects models. The volume-outcome setting is unique in that "volume" reflects both the primary factor under study and also the cluster size, a fact that may well invalidate assumptions inherent in the use of available methods that correct for clustering. Simultaneous evaluation of the effects of hospital volume and surgeon volume is also hampered by the fact that the data are cress-classified, i.e., individual surgeons will perform surgeries at several hospitals. Through a detailed simulation study, the statistical validity of available statistical techniques in this context will be critically evaluated. Our methodological research will heighten awareness of clustering in health policy studies. Upon completion of our research plan, we will make recommendations about various analytic strategies for clustered binary data.

描述（由申请人提供）：在过去的几年中，癌症文献中已经出现了许多研究，将医院和外科医生的程序量与患者结局联系起来。这些研究的结果具有直接的政策影响，因为区域化是一种提高许多不同类型医疗保健的质量和效率的策略。评估医院或外科医生程序量与患者结局之间的关联涉及复杂的统计问题，这是由于观察单位是患者而引起的，但是这些研究包括每个医院或外科医生的多个患者以及多个医院或外科医生。因此，患者的结局往往在医院或外科医生内与同一医院或同一外科医生治疗的患者相关，可能比由医院或外科医生接受相同体积的患者更有可能经历相似的结局。这种现象被称为结果的“聚类”，在存在聚类的标准统计方法的情况下，假定患者结果是独立的，这是无效的。该提案的一般目标是批判性地研究在音量结果研究中使用的广泛统计技术的有效性，例如广义估计方程和随机效应模型。音量结果设置是独一无二的，因为“音量”既反映了正在研究的主要因素，又反映了群集大小，这一事实可能很可能使使用可用方法固有的假设无效。由于数据是由CRESS分类的事实，即单个外科医生将在几家医院进行手术。通过详细的仿真研究，将对这种情况下可用统计技术的统计有效性进行严格评估。我们的方法论研究将提高对卫生政策研究中聚类的认识。完成研究计划后，我们将为群集二进制数据的各种分析策略提出建议。