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find Keyword "Discrete choice experiment" 3 results
  • Preliminary analysis of preferences and willingness to pay for central venous access in patients with breast cancer

    Objective This study aimed to quantitatively investigate the preferences and willingness of patients with breast cancer to pay for central venous access and to provide implications for the clinical selection of appropriate chemotherapy pathways. Methods A discrete-choice experiment survey was conducted to elicit the preferences for central venous access in three hospitals in east, middle and west China. The conditional logit model was used to analyse the relative importance of six central venous access-related attributes: risk of thrombosis, risk of infections, restriction of daily activities, maintenance interval, catheter incision size and out-of-pocket costs. Results The valid data for a total of 103 patients was collected from three hospitals. All six attributes significantly influenced patients’ preferences for central venous access. The risk of thrombosis (RIS=26.0%) and risk of infections (RIS=24.3%) were the top two attributes influencing patients’ preferences for central venous access. To reduce the risk of thrombosis and infection from 12% and 8% to 1%, patients were willing to pay 14 861.2 yuan and 13 907.2 yuan, respectively. The catheter incision size was of least concern (RIS=4.6%); the patients were only willing to pay 2 653.6 yuan for smaller catheter incisions. Conclusion Thrombosis and infection are the primary factors that affect the choice of central venous access for patients with breast cancer. Patients have a sensitive trade-off between safety and out-of-pocket costs; with the change in thrombosis and infection risk, patients’ willingness to pay changes accordingly.

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  • Optimizing the attribute selection process for stated preference study: a study based on best-worst scaling

    ObjectiveTo explore how to determine the attributes of stated preference research more scientifically and reasonably. MethodsBased on the best-worst scaling object case (BWS-1) method, a BWS-1 questionnaire was generated using a balanced incomplete block design. Data collection was conducted among type 2 diabetes mellitus (T2DM) patients in Hainan and Jiangsu provinces. Data analysis was performed using counting analysis and conditional logit model to obtain the priority order of each attribute. ResultsThe results of BWS-1 using the counting and modelling approach showed high consistency. Among the 11 attributes, the top three attributes influencing the preference for second-line antihyperglycemic medications selection in T2DM patients were blood glucose control effectiveness, cardiovascular protection capability, and risk of hypoglycemic events, while the last three factors were dosing frequency, mode of administration and bone fracture. Based on literature review, qualitative research, and BWS-1 results, the seven attributes of discrete choice experiment and best-worst scaling profile case (BWS-2) were determined as follows: treatment efficacy, weight change, hypoglycemic events, gastrointestinal side effects, cardiovascular health, mode of administration and out-of-pocket cost. ConclusionBWS-1 can serve as an effective tool for determining the attributes of stated preference research. However, it is not recommended to solely rely on the priority ranking of BWS-1 results to determine the scope of attributes for stated preference research. It is necessary to conduct a specific analysis in conjunction with the research's policy objectives and real-world circumstances.

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  • A methodological study of health utility measurement based on discrete choice experiment

    The discrete choice experiment (DCE) is a stated preference analysis method to evaluate the impact of multiple factors on individual choice, which has been explored by scholars around the world for health utility measurement. This method is considered to reduce the cognitive burden of traditional utility measurement methods and has high development potential. Through examining empirical studies conducted domestically and internationally that employ DCE for measuring health utility, and drawing on methodological guidelines for constructing DCE models, this article provides an overview of the methodological background of DCE, the practical process used for measuring health utility, and discusses relevant challenges in its application.

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