How Do Treatment Preferences of Patients With Cancer Compare With Those of Oncologists and Family Members? Evidence From a Discrete Choice Experiment in China Journal Articles uri icon

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abstract

  • OBJECTIVES: This study aimed to compare the treatment preference among oncologists, patients with lung cancer, and their family members in China. METHODS: A face-to-face discrete choice experiment survey was conducted among oncologists, patients, and their family members. Participants completed 13 choice sets describing 6 key attributes, namely, overall survival time, risk of severe adverse effect, severity of pain, appetite, physical functioning status, and monthly cost. Mixed logit model and latent class analysis were used to estimate attribute level preference weights and the relative importance (RI) for attributes. The willingness to pay (WTP) and maximum acceptable risk (MAR) were also estimated. The RI, WTP, and MAR of oncologists, patients, and family members were compared. RESULTS: A total of 121 oncologists and 161 dyads of patients and family members completed the survey. Overall survival time, physical functioning status, and pain were the 3 most important attributes across all 3 groups. Oncologists and family members had higher RI on overall survival time than patients (48% and 51% vs 38%). Patients had higher RI on physical functioning status and pain (23% and 14%) than oncologists (13% and 12%) and family members (16% and 11%). For extending survival, patients had the least WTP, and family members had the highest MAR. The latent class analysis identified 2 classes in the patient group and 3 classes in oncologist and family member groups. CONCLUSIONS: There were differences in preferences for survival, risk, quality of life, and costs associated with cancer treatments among patients, oncologists, and family members. This finding highlights the need of involving patients in treatment decision making in China.

authors

  • Zhang, Mengqian
  • He, Xiaoning
  • Wu, Jing
  • Wang, Xinyue
  • Jiang, Qian
  • Xie, Feng

publication date

  • October 2022