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Assessing Women’s Preferences and Preference Modeling for Breast Reconstruction Decision Making

BACKGROUND: Women considering breast reconstruction must make challenging trade-offs among issues that often conflict. It may be useful to quantify possible outcomes using a single summary measure to aid a breast cancer patient in choosing a form of breast reconstruction. METHODS: In this study, we...

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Detalles Bibliográficos
Autores principales: Sun, Clement S., Cantor, Scott B., Reece, Gregory P., Crosby, Melissa A., Fingeret, Michelle C., Markey, Mia K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4120963/
https://www.ncbi.nlm.nih.gov/pubmed/25105083
http://dx.doi.org/10.1097/GOX.0000000000000062
Descripción
Sumario:BACKGROUND: Women considering breast reconstruction must make challenging trade-offs among issues that often conflict. It may be useful to quantify possible outcomes using a single summary measure to aid a breast cancer patient in choosing a form of breast reconstruction. METHODS: In this study, we used multiattribute utility theory to combine multiple objectives to yield a summary value using 9 different preference models. We elicited the preferences of 36 women, aged 32 or older with no history of breast cancer, for the patient-reported outcome measures of breast satisfaction, psychosocial well-being, chest well-being, abdominal well-being, and sexual well-being as measured by the BREAST-Q in addition to time lost to reconstruction and out-of-pocket cost. Participants ranked hypothetical breast reconstruction outcomes. We examined each multiattribute utility preference model and assessed how often each model agreed with participants’ rankings. RESULTS: The median amount of time required to assess preferences was 34 minutes. Agreement among the 9 preference models with the participants ranged from 75.9% to 78.9%. None of the preference models performed significantly worse than the best-performing risk-averse multiplicative model. We hypothesize an average theoretical agreement of 94.6% for this model if participant error is included. There was a statistically significant positive correlation with more unequal distribution of weight given to the 7 attributes. CONCLUSIONS: We recommend the risk-averse multiplicative model for modeling the preferences of patients considering different forms of breast reconstruction because it agreed most often with the participants in this study.