Methods Inf Med - The nested structure of cancer symptoms. Implications for analyzing co-occurrence and managing symptoms.


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JECTIVE: 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-occurrence 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.

Resumo Limpo

jectiv although mani cancer patient experi multipl concurr symptom studi either focus analysi singl symptom use method factor analysi make priori assumpt data structur articl address limit first visual explor data identifi pattern cooccurr multipl symptom use insight select develop quantit measur analyz valid resultsmethod use network visual cancer patient report symptom quantit analyz observ pattern use degre symptom overlap patient degre symptom cluster use network modular cluster symptom base agglom hierarch cluster degre nested symptom base frequent cooccur symptom differ size symptom set result valid assess statist signific quantit measur comparison random network size distributionresult cancer symptom tend cooccur nest structur small set symptom cooccur mani patient progress larger set symptom cooccur among patientsconclus result suggest cancer symptom cooccur nest pattern oppos distinct cluster therebi demonstr valu exploratori network analys reveal complex relationship patient symptom research also extend method explor symptom cooccurr includ method quantifi degre symptom overlap examin nest cooccurr cooccurr data final analysi also suggest implic design system assist symptom assess manag main limit studi one dataset consid futur studi attempt replic result new data

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