Congruence between latent class and k-modes analyses in the identification of oncology patients with distinct symptom experiences

Papachristou, Nikoloas and Barnaghi, Payam and Cooper, Bruce A. and Hu, Xiao and Maguire, Roma and Apostolidis, Kathi and Armes, Jo and Conley, Yvette P. and Hammer, Marilyn and Katsaragakis, Stylianos and Kober, Kord M. and Levine, Jon D. and McCann, Lisa and Patiraki, Elisabeth and Paul, Steven M. and Ream, Emma and Wright, Fay and Miaskowski, Christine (2017) Congruence between latent class and k-modes analyses in the identification of oncology patients with distinct symptom experiences. Journal of Pain and Symptom Management. ISSN 0885-3924 (https://doi.org/10.1016/j.jpainsymman.2017.08.020)

[thumbnail of Papchristou-etal-JPSM-2018-k-modes-analyses-in-the-identification-of-oncology-patients-with-distinct-symptom-experiences]
Preview
 Text. Filename: Papchristou_etal_JPSM_2018_k_modes_analyses_in_the_identification_of_oncology_patients_with_distinct_symptom_experiences.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (440kB)| Preview

Abstract

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

ORCID iDs

Papachristou, Nikoloas, Barnaghi, Payam, Cooper, Bruce A., Hu, Xiao, Maguire, Roma ORCID logoORCID: https://orcid.org/0000-0001-7935-3447, Apostolidis, Kathi, Armes, Jo, Conley, Yvette P., Hammer, Marilyn, Katsaragakis, Stylianos, Kober, Kord M., Levine, Jon D., McCann, Lisa ORCID logoORCID: https://orcid.org/0000-0002-5322-5778, Patiraki, Elisabeth, Paul, Steven M., Ream, Emma, Wright, Fay and Miaskowski, Christine;
CORE (COnnecting REpositories)