Brookhart M Alan, Solomon Daniel H, Wang Philip, Glynn Robert J, Avorn Jerry, Schneeweiss Sebastian
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital-Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA 02120, USA.
J Clin Epidemiol. 2006 Jan;59(1):18-25. doi: 10.1016/j.jclinepi.2005.07.005. Epub 2005 Nov 7.
Data on therapeutic decision making have a multilevel structure that can include patient-, provider-, and facility-level variables. A statistical method is presented for attributing explained variation in patient care to different levels of aggregation in a multilevel model with the aim of prioritizing and targeting quality improvement interventions.
The proposed method is used in an analysis of adherence to evidence-based guidelines for the care of patients at risk of osteoporosis. Explained variation from a multilevel model of appropriate care is partitioned across patient-, physician-, and clinic-level factors.
The combination of patient, physician, and clinic factors explained 20.0% of the variation in patient care. Individual physician effects explained 14.0% of the variation in the data; however, more than half of this explained variation could have been attributed to the individual clinic effect. Patient fixed effects alone explained 13.4% of the variation in the observed clinical decisions.
The proposed approach is an intuitive and statistically valid method for attributing explained variation in a multilevel analysis of therapeutic decision making.
治疗决策数据具有多层次结构，可包含患者、提供者和机构层面的变量。本文提出一种统计方法，用于在多层次模型中将患者护理中已解释的变异归因于不同层次的汇总，目的是对质量改进干预措施进行优先级排序和目标设定。
所提出的方法用于分析对骨质疏松症高危患者护理的循证指南的依从性。适当护理的多层次模型中已解释的变异在患者、医生和诊所层面的因素之间进行划分。
患者、医生和诊所因素的组合解释了患者护理中20.0%的变异。个体医生效应解释了数据中14.0%的变异；然而，超过一半的已解释变异可能归因于个体诊所效应。仅患者固定效应就解释了观察到的临床决策中13.4%的变异。
所提出的方法是一种直观且统计有效的方法，用于在治疗决策的多层次分析中归因已解释的变异。
