a social network analysis of treatment discoveries in cancer社会网络分析在癌症治疗的发现.pdfVIP

A Social Network Analysis of Treatment Discoveries in Cancer 1 1 2 3 1,4 Athanasios Tsalatsanis *, Laura Barnes , Iztok Hozo , John Skvoretz , Benjamin Djulbegovic 1 Center for Evidence-Based Medicine and Health Outcomes Research, University of South Florida, Tampa, Florida, United States of America, 2 Department of Mathematics, Indiana University Northwest, Gary, Indiana, United States of America, 3 Department of Sociology, University of South Florida, Tampa, Florida, United States of America, 4 H. Lee Moffitt Cancer Center Research Institute, Tampa, Florida, United States of America Abstract Controlled clinical trials are widely considered to be the vehicle to treatment discovery in cancer that leads to significant improvements in health outcomes including an increase in life expectancy. We have previously shown that the pattern of therapeutic discovery in randomized controlled trials (RCTs) can be described by a power law distribution. However, the mechanism generating this pattern is unknown. Here, we propose an explanation in terms of the social relations between researchers in RCTs. We use social network analysis to study the impact of interactions between RCTs on treatment success. Our dataset consists of 280 phase III RCTs conducted by the NCI from 1955 to 2006. The RCT networks are formed through trial interactions formed i) at random, ii) based on common characteristics, or iii) based on treatment success. We analyze treatment success in terms of survival hazard ratio as a function of the network structures. Our results show that the discovery process displays power law if there are preferential interactions between trials that may stem from researchers’ tendency to interact selectively with established and su