Logo image
Back
GraphCHASUR: a change-point-aware, survival-informed framework for temporal network analysis of adverse events
Dissertation   Open access

GraphCHASUR: a change-point-aware, survival-informed framework for temporal network analysis of adverse events

Niusha Jafari
Doctor of Philosophy (Ph.D.), Drexel University
Mar 2026
DOI:
https://doi.org/10.17918/00011295
pdf
Jafari_Niusha_20265.05 MBDownloadView
PDFOpen Access (License Unspecified),  Open Access

Abstract

Adverse event analysis Co-occurrence networks Graph-based modeling Mortality prediction Safety signal detection Temporal networks
Adverse Events (AEs) in clinical trials and healthcare settings are undesired reactions to treatment. These data are high-dimensional, sparse, and interrelated, and their characteristics and interactions may evolve over time. These properties make their analysis very challenging and hence impact effective safety monitoring. Conventional methods often overlook the temporal dependencies, and interrelatedness of AEs, and this limits their ability to fully detect emerging safety signals or identify critical intervention points. We introduce a novel framework, GraphCHASUR, which rooted in temporal network frame. It integrates AE incidence rates which are survival-informed, change point detection methodology, and graph structure to model evolving AE dynamics while capturing AEs relations. In GraphCHASUR, the entire course of treatment is segmented into data-informed snapshots by applying change point detection techniques on weighted AE incidence rates. Once the snapshots are detected, a graph develops within each segment by having AEs as nodes in co-occurrence networks. Two AEs form an edge if they occurred together in at least one patient, these edges will next be weighted by severity/toxicity and timing of the co-occurrence onsets, aggregated across all observed AE pairs. This process produces evolving networks that capture shifts in toxicity patterns while accounting for AEs inter-connections. Once networks are developed, relevant metrics such as centrality measures, node and edge strengths, in addition to community structure are extracted and will be used for downstream predictive modeling of clinical outcomes. We assessed the performance of the network derived features using simulated data. The results demonstrated that the GraphCHASUR-derived variables improved mortality prediction compared to the models with only patients features at baseline, such age, gender, ECOG, and biomarker status. The framework also outperformed models incorporating crude count of AE. In comparison with deep learning approaches, such as LSTM, the performance was quite comparable while the GraphCHASUR features remained highly interpretable. The network divided feature modeling achieving the highest F1 score (0.84) and recall (0.86) in predating patient mortality. Application to a phase 3 colorectal cancer trial (N = 1183) revealed clinically meaningful shifts in toxicity patterns, transitioning from early gastrointestinal symptoms to later hematologic and immune-related toxicities. The identified community structures and safety signals aligned to what we observed in the literature for this particular investigational treatment. By addressing the limitations of static AE summaries with dynamic network-based modeling, GraphCHASUR provides an interpretable, time-aware framework for safety analysis. The framework supports timely detection of safety signals and enables a clearer understanding of toxicity progression in high-dimensional AE data.

Metrics

1 Record Views

Details

Logo image