Summary
Objective: Although many cancer patients experience multiple concurrent symptoms, most studies
have either focused on the analysis of single symptoms, or have used methods such
as factor analysis that make a priori assumptions about how the data is structured. This article addresses both limitations
by first visually exploring the data to identify patterns in the co-occurrence of
multiple symptoms, and then using those insights to select and develop quantitative
measures to analyze and validate the results.
Methods: We used networks to visualize how 665 cancer patients reported 18 symptoms, and then
quantitatively analyzed the observed patterns using degree of symptom overlap between
patients, degree of symptom clustering using network modularity, clustering of symptoms
based on agglomerative hierarchical clustering, and degree of nestedness of the symptoms
based on the most frequently co-occurring symptoms for different sizes of symptom
sets. These results were validated by assessing the statistical significance of the
quantitative measures through comparison with random networks of the same size and
distribution.
Results: The cancer symptoms tended to co-occur in a nested structure, where there was a small
set of symptoms that co-occurred in many patients, and progressively larger sets of
symptoms that co-occurred among a few patients.
Conclusions: These results suggest that cancer symptoms co-occur in a nested pattern as opposed
to distinct clusters, thereby demonstrating the value of exploratory network analyses
to reveal complex relationships between patients and symptoms. The research also extends
methods for exploring symptom co-occurrence, including methods for quantifying the
degree of symptom overlap and for examining nested co-occurrence in co-occur-rence
data. Finally, the analysis also suggested implications for the design of systems
that assist in symptom assessment and management. The main limitation of the study
was that only one dataset was considered, and future studies should attempt to replicate
the results in new data.
Keywords
Network visualization and analysis - cooccurrence of cancer symptoms - symptom management
