Objectives: To determine if subgroups of individuals with prostate cancer and their partners could be identified based on their distinct symptom profiles and to identify and characterize subgroups based on sociodemographic and cancer characteristics.
Sample & setting: 263 individuals with prostate cancer and 263 partners recruited from three academic cancer centers in the Midwest.
Methods & variables: Latent class analysis was applied to divide individuals into subgroups based on symptom prevalence. Multinomial logistic regression models were used to estimate the prevalence of each symptom, predict subgroup membership, and adjust for direct or indirect effects of covariates on the symptoms.
Results: Three distinct subgroups (low, moderate, and high symptoms) were identified among individuals with prostate cancer and partners, respectively. Education and household income of individuals with prostate cancer were associated with different symptom burdens. Partners' household income differentiated among the subgroups.
Implications for nursing: Understanding the influence of sociodemographic and cancer characteristics can inform risk stratification and tailored symptom management interventions.
Keywords: cancer caregivers; latent class analysis; prostate cancer; symptom cluster research.