Among many other examples are social support networks that may serve to improve individual well-being by providing psychosocial or tangible resources ( Berkman and Syme 1979) peer group influence networks that may heighten-or protect against-the risk of substance abuse ( Unger and Chen 1999) or shape decisions about contraceptive use ( Valente et al. patients, nurses, physicians, and hospitals), networks are of increasing interest to health services researchers. Since many medical and health-related phenomena involve interdependent actors (e.g. Social network analysis studies structures of relationships linking individuals (or other social units, such as organizations) and interdependencies in behavior or attitudes related to configurations of social relations. Complexities in estimating both types of models arise due to the complex correlation structures among outcome measures. 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. This article reviews fundamentals of, and recent innovations in, social network analysis using a physician influence network as an example. 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.

Social network studies have been pursued for some time in social science disciplines, where numerous descriptive methods for analyzing them have been proposed. Many questions about the social organization of medicine and health services involve interdependencies among social actors that may be depicted by networks of relationships.

Cycle of network analysis definition