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Methods Inf Med 2010; 49(06): 581-591
DOI: 10.3414/ME09-01-0083
Original Articles
Schattauer GmbH

The Nested Structure of Cancer Symptoms

Implications for Analyzing Co-occurrence and Managing Symptoms
S. K. Bhavnani
1   Center for Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
2   Michigan Institute for Clinical and Health Research, Ann Arbor, MI, USA
7   currenty at Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA
,
G. Bellala
3   Electrical Engineering and Computer Science, Ann Arbor, MI, USA
,
A. Ganesan
3   Electrical Engineering and Computer Science, Ann Arbor, MI, USA
,
R. Krishna
4   Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
,
P. Saxman
2   Michigan Institute for Clinical and Health Research, Ann Arbor, MI, USA
,
C. Scott
1   Center for Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
3   Electrical Engineering and Computer Science, Ann Arbor, MI, USA
,
M. Silveira
5   Health Services Research and Development, VA Center for Clinical Management Research, Ann Arbor, MI, USA
,
C. Given
6   Department of Family Medicine, Michigan State University, East Lansing, MI, USA
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Publikationsverlauf

received: 09. September 2009

accepted: 04. April 2010

Publikationsdatum:
18. Januar 2018 (online)

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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