title: Evolution of treatment regimens in multiple myeloma: a social network analysis.
abstract: BACKGROUND
  Randomized controlled trials (RCTs) are considered the gold standard for assessing the efficacy of new treatments compared to standard treatments. However, the reasoning behind treatment selection in RCTs is often unclear. Here, we focus on a cohort of RCTs in multiple myeloma (MM) to understand the patterns of competing treatment selections.
  
  
  METHODS
  We used social network analysis (SNA) to study relationships between treatment regimens in MM RCTs and to examine the topology of RCT treatment networks. All trials considering induction or autologous stem cell transplant among patients with MM were eligible for our analysis. Medline and abstracts from the annual proceedings of the American Society of Hematology and American Society for Clinical Oncology, as well as all references from relevant publications were searched. We extracted data on treatment regimens, year of publication, funding type, and number of patients enrolled. The SNA metrics used are related to node and network level centrality and to node positioning characterization.
  
  
  RESULTS
  135 RCTs enrolling a total of 36,869 patients were included. The density of the RCT network was low indicating little cohesion among treatments. Network Betweenness was also low signifying that the network does not facilitate exchange of information. The maximum geodesic distance was equal to 4, indicating that all connected treatments could reach each other in four "steps" within the same pathway of development. The distance between many important treatment regimens was greater than 1, indicating that no RCTs have compared these regimens.
  
  
  CONCLUSIONS
  Our findings show that research programs in myeloma, which is a relatively small field, are surprisingly decentralized with a lack of connectivity among various research pathways. As a result there is much crucial research left unexplored. Using SNA to visually and analytically examine treatment networks prior to designing a clinical trial can lead to better designed studies.
authors.affiliation: Department of Internal Medicine, Division of Evidence-Based Medicine & Health Outcomes Research, University of South Florida, Tampa, Florida, United States of America.
authors.forename: Helen
authors.initials: H
authors.lastname: Mahony
authors.affiliation: Department of Internal Medicine, Division of Evidence-Based Medicine & Health Outcomes Research, University of South Florida, Tampa, Florida, United States of America.
authors.forename: Athanasios
authors.initials: A
authors.lastname: Tsalatsanis
authors.affiliation: Department of Internal Medicine, Division of Evidence-Based Medicine & Health Outcomes Research, University of South Florida, Tampa, Florida, United States of America.
authors.forename: Ambuj
authors.initials: A
authors.lastname: Kumar
authors.affiliation: Department of Internal Medicine, Division of Evidence-Based Medicine & Health Outcomes Research, University of South Florida, Tampa, Florida, United States of America; H. Lee Moffitt Cancer Center & Research Institute, Department of Hematology and Health Outcomes and Behavior, Tampa, Florida, United States of America.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Djulbegovic
mesh_terms: D000465:Algorithms; D006801:Humans; D009101:Multiple Myeloma / Q000188:drug therapy*; D016032:Randomized Controlled Trials as Topic; D012944:Social Support