Congruence Between Latent Class and K-Modes Analyses in the Identification of Oncology Patients With Distinct Symptom Experiences

Nikoloas Papachristou, Payam Barnaghi, Bruce A. Cooper, Xiao Hu, Roma Maguire, Kathi Apostolidis, Jo Armes, Yvette P. Conley, Marilyn Hammer, Stylianos Katsaragakis, Kord M. Kober, Jon D. Levine, Lisa McCann, Elisabeth Patiraki, Steven M. Paul, Emma Ream, Fay Wright, Christine Miaskowski

Research output: Contribution to journalArticlepeer-review


Context: Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods. Objectives: The objective of this study was to evaluate the concordance between the number and types of subgroups of patients with distinct symptom profiles using latent class analysis and K-modes analysis. Methods: Using data on the occurrence of 25 symptoms from the Memorial Symptom Assessment Scale, that 1329 patients completed prior to their next dose of chemotherapy (CTX), Cohen's kappa coefficient was used to evaluate for concordance between the two analytic methods. For both latent class analysis and K-modes, differences among the subgroups in demographic, clinical, and symptom characteristics, as well as quality of life outcomes were determined using parametric and nonparametric statistics. Results: Using both analytic methods, four subgroups of patients with distinct symptom profiles were identified (i.e., all low, moderate physical and lower psychological, moderate physical and higher Psychological, and all high). The percent agreement between the two methods was 75.32%, which suggests a moderate level of agreement. In both analyses, patients in the all high group were significantly younger and had a higher comorbidity profile, worse Memorial Symptom Assessment Scale subscale scores, and poorer QOL outcomes. Conclusion: Both analytic methods can be used to identify subgroups of oncology patients with distinct symptom profiles. Additional research is needed to determine which analytic methods and which dimension of the symptom experience provide the most sensitive and specific risk profiles.

Original languageEnglish (US)
Pages (from-to)318-333.e4
JournalJournal of Pain and Symptom Management
Issue number2
StatePublished - Feb 2018


  • Symptom clusters
  • cancer
  • chemotherapy
  • clustering
  • k-modes analysis
  • latent class analysis
  • machine learning

ASJC Scopus subject areas

  • General Nursing
  • Clinical Neurology
  • Anesthesiology and Pain Medicine


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