Examining association between cohesion and diversity in collaboration networks of pharmaceutical clinical trials with drug approvals. (8th November 2020)

• Record Type: Journal Article
• Authors: Lin, Gary; Siddiqui, Sauleh; Bernstein, Jen; Martinez, Diego A; Gardner, Lauren; Albright, Tenley; Igusa, Takeru
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 1(2021)
• Page Start: 62
• Page End: 70
• Publication Date: 2020-11-08
• Subjects: complex system -- science of science -- clinical research -- collaboration network -- network science; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa243
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Clinical trials ensure that pharmaceutical treatments are safe, efficacious, and effective for public consumption, but are extremely complex, taking up to 10 years and $2.6 billion to complete. One main source of complexity arises from the collaboration between actors, and network science methodologies can be leveraged to explore that complexity. We aim to characterize collaborations between actors in the clinical trials context and investigate trends of successful actors. Materials and Methods: We constructed a temporal network of clinical trial collaborations between large and small-size pharmaceutical companies, academic institutions, nonprofit organizations, hospital systems, and government agencies from public and proprietary data and introduced metrics to quantify actors' collaboration network structure, organizational behavior, and partnership characteristics. A multivariable regression analysis was conducted to determine the metrics' relationship with success. Results: We found a positive correlation between the number of successful approved trials and interdisciplinary collaborations measured by a collaboration diversity metric ( P < .01). Our results also showed a negative effect of the local clustering coefficient ( P < .01) on the success of clinical trials. Large pharmaceutical companies have the lowest local clustering coefficient and more diversity in partnerships across biomedical specializations. Conclusions: Large pharmaceutical companies are more likely to collaborate with a wider range of actors from other specialties, especially smaller industry actors who are newcomers in clinical research, resulting in exclusive access to smaller actors. Future investigations are needed to show how concentrations of influence and resources might result in diminished gains in treatment development.