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Assessing Women's Preferences and Preference Modeling for Breast Reconstruction Decision-Making.

Sun CS, Cantor SB, Reece GP, Crosby MA, Fingeret MC, Markey MK - Plast Reconstr Surg Glob Open (2014)

Bottom Line: 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 seven attributes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX ; Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.

ABSTRACT

Background: Women considering breast reconstruction must make challenging trade-offs amongst 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 nine 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 wellbeing 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 nine 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 seven 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.

No MeSH data available.


Related in: MedlinePlus

Three risk models: risk neutral, risk averse, and sigmoidal. The BREAST-Q propriety scoring algorithm models preferences for its measures in an inverse-sigmoidal fashion with risk aversion for lower values and risk seeking for higher values.
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Figure 2: Three risk models: risk neutral, risk averse, and sigmoidal. The BREAST-Q propriety scoring algorithm models preferences for its measures in an inverse-sigmoidal fashion with risk aversion for lower values and risk seeking for higher values.

Mentions: Attribute values such as time and cost must first be converted into a u-value. We considered 3 types of risk models: (1) risk neutral, (2) risk averse, and (3) risk averse-preferring or sigmoidal (Fig. 2). For instance, with respect to out-of- pocket reconstruction cost, a risk neutral participant would place a value of $1000 on a 50-50 chance of a reconstruction that costs $2000 and a reconstruction that costs $0. A risk-averse participant would value the same gamble at more than $1000. A risk-preferring participant would value the gamble at less than $1000.


Assessing Women's Preferences and Preference Modeling for Breast Reconstruction Decision-Making.

Sun CS, Cantor SB, Reece GP, Crosby MA, Fingeret MC, Markey MK - Plast Reconstr Surg Glob Open (2014)

Three risk models: risk neutral, risk averse, and sigmoidal. The BREAST-Q propriety scoring algorithm models preferences for its measures in an inverse-sigmoidal fashion with risk aversion for lower values and risk seeking for higher values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4120963&req=5

Figure 2: Three risk models: risk neutral, risk averse, and sigmoidal. The BREAST-Q propriety scoring algorithm models preferences for its measures in an inverse-sigmoidal fashion with risk aversion for lower values and risk seeking for higher values.
Mentions: Attribute values such as time and cost must first be converted into a u-value. We considered 3 types of risk models: (1) risk neutral, (2) risk averse, and (3) risk averse-preferring or sigmoidal (Fig. 2). For instance, with respect to out-of- pocket reconstruction cost, a risk neutral participant would place a value of $1000 on a 50-50 chance of a reconstruction that costs $2000 and a reconstruction that costs $0. A risk-averse participant would value the same gamble at more than $1000. A risk-preferring participant would value the gamble at less than $1000.

Bottom Line: 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 seven attributes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX ; Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.

ABSTRACT

Background: Women considering breast reconstruction must make challenging trade-offs amongst 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 nine 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 wellbeing 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 nine 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 seven 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.

No MeSH data available.


Related in: MedlinePlus