O'Malley A James, Marsden Peter V
Associate Professor of Statistics, Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115-5899.
Health Serv Outcomes Res Methodol. 2008 Dec 1;8(4):222-269. doi: 10.1007/s10742-008-0041-z.
Many questions about the social organization of medicine and health services involve interdependencies among social actors that may be depicted by networks of relationships. Social network studies have been pursued for some time in social science disciplines, where numerous descriptive methods for analyzing them have been proposed. More recently, interest in the analysis of social network data has grown among statisticians, who have developed more elaborate models and methods for fitting them to network data. This article reviews fundamentals of, and recent innovations in, social network analysis using a physician influence network as an example. After introducing forms of network data, basic network statistics, and common descriptive measures, it describes two distinct types of statistical models for network data: individual-outcome models in which networks enter the construction of explanatory variables, and relational models in which the network itself is a multivariate dependent variable. Complexities in estimating both types of models arise due to the complex correlation structures among outcome measures.
许多关于医学和卫生服务社会组织的问题涉及社会行为者之间的相互依存关系，这些关系可以用关系网络来描述。社会网络研究在社会科学学科中已经开展了一段时间，在这些学科中已经提出了许多分析社会网络的描述性方法。最近，统计学家对社会网络数据分析的兴趣有所增加，他们开发了更精细的模型和方法来将其应用于网络数据。本文以医生影响网络为例，回顾社会网络分析的基本原理和最新创新。在介绍网络数据的形式、基本网络统计和常见描述性指标之后，本文描述了两种不同类型的网络数据统计模型：个体结果模型，其中网络参与解释变量的构建；关系模型，其中网络本身是一个多变量因变量。由于结果度量之间复杂的相关结构，估计这两种类型的模型都会出现复杂性。
