Centrality in Collaboration: A Novel Algorithm for Social Partitioning Gradients in Community Detection for Multiple Oncology Clinical Trial Enrollments

TL;DR

This paper introduces a new community detection algorithm that effectively identifies collaboration networks among oncologists, aiding epidemiological research and clinical trial enrollment analysis.

Contribution

The study presents a novel algorithm for social partitioning that outperforms existing methods in interpretability and utility for epidemiological studies.

Findings

The new algorithm produces intuitive and informative intervention groups.

Girvan-Newman fails to differentiate intervention communities effectively.

Louvain groups interventions in an unclear manner.

Abstract

Patients at a comprehensive cancer center who do not achieve cure or remission following standard treatments often become candidates for clinical trials. Patients who participate in a clinical trial may be suitable for other studies. A key factor influencing patient enrollment in subsequent clinical trials is the structured collaboration between oncologists and most responsible physicians. Possible identification of these collaboration networks can be achieved through the analysis of patient movements between clinical trial intervention types with social network analysis and community detection algorithms. In the detection of oncologist working groups, the present study evaluates three community detection algorithms: Girvan-Newman, Louvain and an algorithm developed by the author. Girvan-Newman identifies each intervention as their own community, while Louvain groups interventions in a manner that is difficult to interpret. In contrast, the author's algorithm groups interventions in a way that is both intuitive and informative, with a gradient evident in social partitioning that is particularly useful for epidemiological research. This lays the groundwork for future subgroup analysis of clustered interventions.