Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2026
Beyond the Average: Modeling Individual-Specific Preferences for Ulcerative Colitis Surgery.
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Wickramasekera Nyantara, Rowen Donna, Brown Steven, Hole Arne Risa
Аннотация
OBJECTIVES: Surgical decisions for ulcerative colitis are complex and preference-sensitive. This study aimed to assess patient preferences for surgical treatments, quantify preference heterogeneity, and examine individual-specific preferences to inform decision making.
METHODS: Patient preferences were elicited using a discrete choice experiment. A rigorous selection process involving focus groups and interviews with clinicians and patients resulted in 7 key attributes. Each task included 2 unlabeled surgical alternatives and a medication opt-out. A D-efficient fractional factorial design was generated. The survey was pilot tested using "think-aloud" interviews. Data were analyzed using multinomial and mixed logit models, with conditional mean coefficients used to estimate individual-specific choice probabilities.
RESULTS: Three hundred and fifty patients completed the survey. Results showed significant preference heterogeneity for most attributes. The preference for the medication opt-out revealed a multimodal conditional distribution, clustering patients who strongly preferred, were indifferent to, or disliked medication. The interaction term "planning to have children" fully explained the preference heterogeneity in the fertility attribute. A gender interaction term showed that male patients had a stronger negative preference for a stoma. Choice probabilities showed individual differences; some patients had a 97.98% probability of preferring medication, whereas others had only a 0.09% probability, instead showing a high preference for surgical options.
CONCLUSIONS: This study demonstrates the value of using conditional distributions to examine preference heterogeneity. Simpler models failed to reveal the wide range of preferences present in the data. Conditional choice probabilities can be used to better understand how different patients make treatment decisions.
METHODS: Patient preferences were elicited using a discrete choice experiment. A rigorous selection process involving focus groups and interviews with clinicians and patients resulted in 7 key attributes. Each task included 2 unlabeled surgical alternatives and a medication opt-out. A D-efficient fractional factorial design was generated. The survey was pilot tested using "think-aloud" interviews. Data were analyzed using multinomial and mixed logit models, with conditional mean coefficients used to estimate individual-specific choice probabilities.
RESULTS: Three hundred and fifty patients completed the survey. Results showed significant preference heterogeneity for most attributes. The preference for the medication opt-out revealed a multimodal conditional distribution, clustering patients who strongly preferred, were indifferent to, or disliked medication. The interaction term "planning to have children" fully explained the preference heterogeneity in the fertility attribute. A gender interaction term showed that male patients had a stronger negative preference for a stoma. Choice probabilities showed individual differences; some patients had a 97.98% probability of preferring medication, whereas others had only a 0.09% probability, instead showing a high preference for surgical options.
CONCLUSIONS: This study demonstrates the value of using conditional distributions to examine preference heterogeneity. Simpler models failed to reveal the wide range of preferences present in the data. Conditional choice probabilities can be used to better understand how different patients make treatment decisions.