Analysis Methods for Volume-Outcome Studies

描述（由申请人提供）：在过去的几年中，在癌症文献中出现了大量的研究，将医院和外科手术的数量与患者的结果联系起来。这些研究的结果具有直接的政策影响，因为区域化是一项经过深思熟虑的战略，旨在提高许多不同类型卫生保健的质量和效率。评估医院或外科医生的手术量与患者预后之间的关系涉及复杂的统计问题，这是因为观察单位是患者，但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，患者的治疗结果在医院或外科医生之间往往是相关的，也就是说，在同一家医院或由同一名外科医生治疗的患者，可能比在同一家医院或相同容量的外科医生治疗的患者更有可能经历相似的结果。这种现象被称为结果的“聚类”，在存在聚类的情况下，假设患者结果是独立的标准统计方法是无效的。本提案的总体目标是严格检查在体积结果研究（如广义估计方程和随机效应模型）中使用的广泛可用的统计技术的有效性。体积-结果设置是独特的，因为“体积”既反映了所研究的主要因素，也反映了聚类大小，这一事实很可能使使用现有方法修正聚类所固有的假设无效。同时评估医院数量和外科医生数量的影响也受到数据分类这一事实的阻碍，即单个外科医生将在几家医院进行手术。通过详细的模拟研究，在这种情况下，可用的统计技术的统计有效性将被严格评估。我们的方法学研究将提高对卫生政策研究中聚类的认识。在完成我们的研究计划后，我们将对聚类二进制数据的各种分析策略提出建议。

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.