Identifying patterns of adaptation in breast cancer patients with cancer-related fatigue using response shift analyses at subgroup level.
Fiche publication
Date publication
octobre 2017
Journal
Cancer medicine
Auteurs
Membres identifiés du Cancéropôle Est :
Pr GUILLEMIN Francis, Dr ROTONDA Christine
Tous les auteurs :
Salmon M, Blanchin M, Rotonda C, Guillemin F, Sébille V
Lien Pubmed
Résumé
Fatigue is the most prevalent symptom in breast cancer. It might be perceived differently among patients over time as a consequence of the differing patients' adaptation and psychological adjustment to their cancer experience which can be related to response shift (RS). RS analyses can provide important insights on patients' adaptation to cancer but it is usually assumed that RS occurs in the same way in all individuals which is unrealistic. This study aimed to identify patients' subgroups in which different RS effects on self-reported fatigue could occur over time using a combination of methods for manifest and latent variables. The FATSEIN study comprised 466 breast cancer patients followed over a 2-year period. Fatigue was measured with the Multidimensional Fatigue Inventory questionnaire (MFI-20) during 10 visits. A novel combination of Mixed Models, Growth Mixture Modeling, and Structural Equation Modeling was used to assess the occurrence of RS in fatigue changes to identify subgroups displaying different RS patterns over time. An increase in fatigue was evidenced over the 8-month follow-up, followed by a decrease between the 8- and 24-month. Four latent classes of patients were identified. Different RS patterns were detected in all latent classes between the inclusion and 8 months (last cycle of chemotherapy). No RS was evidenced between 8- and 24-month. Several RS effects were evidenced in different groups of patients. Women seemed to adapt differently to their treatment and breast cancer experience possibly indicating differing needs for medical/psychological support.
Mots clés
Breast cancer, fatigue, patient-reported outcome measures, response-shift, structural equation modeling
Référence
Cancer Med. 2017 Oct;: