Objectives: This article explains how to optimize Bayesian D-efficient discrete choice experiment (DCE) designs for the estimation of quality-adjusted life year (QALY) tariffs that are unconfounded by respondents' time preferences.
Methods: The calculation of Bayesian D-errors is explained for DCE designs that allow for the disentanglement of respondents' time and health-state preferences. Time preferences are modelled via an exponential, hyperbolic, or power discount function and the performance of the proposed DCE designs is compared with that of several conventional DCE designs that do not take nonlinear time preferences into account.
Results: Based on the achieved D-error, asymptotic standard error, and estimated sample size to obtain statistically significant estimates of the discount rate parameters, the proposed designs outperform the conventional DCE designs.
Conclusions: We recommend that applied researchers use appropriately optimized DCE designs for the estimation of QALY tariffs that are corrected for time preferences. The TPC-QALY software package that accompanies this article makes the recommended designs easily accessible for health-state valuation researchers.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jval.2019.05.014 | DOI Listing |
EClinicalMedicine
January 2025
School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Background: Discrete choice experiments (DCEs) are increasingly used to inform the design of health products and services. It is essential to understand the extent to which DCEs provide reliable predictions outside of experimental settings in real-world decision-making situations. We aimed to compare the prediction accuracy of stated preferences with real-world choices, as modelled from DCE data.
View Article and Find Full Text PDFNurs Health Sci
March 2025
School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
The global aging demographic trend has led to an increased demand for home care services among older people. We measured the preferences and willingness to pay (WTP) of older adults in China for home care services using a discrete choice experiment (DCE) design. A total of 496 valid responses from older adults were included.
View Article and Find Full Text PDFJ Oleo Sci
January 2025
School of Engineering Science, Kochi University of Technology.
A simple synthetic method for pinocembrin from cinnamic acid and 1,3,5-trihydroxybenzene was provided. This method can be performed in one-pot two steps reaction using inexpensive chemical reagents, whereas conventional methods need multiple steps from somewhat expensive starting reagents. The experimental procedure is facilitated, that is, to a DMF solution of cinnamoyl chloride generated in situ, a solution of 1,3,5-trihydroxybenzene and AlCl in DCE/PhNO was added, and the resultant mixture was heated to afford pinocembrin.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
Background: Given the increasing recognition of the value of greater integration of physical and mental health services for children and young people, we aimed to evaluate preferences among parents for the characteristics associated with integrated health service provision for two conditions (eating disorders, functional symptom disorders).
Methods: Two discrete choice experiments (DCEs) were conducted, using electronic surveys. Participants were adult parents of children and young people.
BMC Med Educ
January 2025
Research Center for Environmental Determinants of Health, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Aims: This study evaluates both financial and non-financial preferences of nursing students to choose a hospital for work in future.
Background: In Iran's healthcare system, the persistent shortage and uneven distribution of nurses have been significant challenges. Addressing such issues requires attention to nurses' preferences, which can be instrumental in designing effective interventions.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!