Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Center for Data Science in Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
J Am Med Inform Assoc. 2021 Jan 15;28(1):62-70. doi: 10.1093/jamia/ocaa243.
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.
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.
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.
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.
临床试验确保了药物治疗对公众是安全、有效和有益的,但却极其复杂,需要长达 10 年的时间和 26 亿美元才能完成。造成这种复杂性的一个主要原因是参与者之间的合作,而网络科学方法可以用来探索这种复杂性。我们旨在描述临床试验背景下参与者之间的合作,并研究成功参与者的趋势。
我们从公共和私人数据中构建了一个大型和小型制药公司、学术机构、非营利组织、医院系统和政府机构之间临床试验合作的时间网络,并引入了一些指标来量化参与者的合作网络结构、组织行为和伙伴关系特征。进行了多变量回归分析,以确定这些指标与成功的关系。
我们发现,成功批准试验的数量与合作多样性指标(通过合作多样性指标衡量)呈正相关(P <.01)。我们的结果还表明,临床试验成功与局部聚类系数呈负相关(P <.01)。大型制药公司的局部聚类系数最低,与生物医学专业的其他参与者的合作更加多样化。
大型制药公司更有可能与来自其他专业的更广泛的参与者合作,特别是在临床研究中崭露头角的规模较小的行业参与者,从而获得对规模较小的参与者的独家访问权。需要进一步研究如何集中影响力和资源可能会导致治疗开发收益减少。