scholarly journals Are canadians different from americans in stated preferences for health? valuing EQ-5D-5L health states using discrete choice experiments

2013 ◽  
Vol 16 (3) ◽  
pp. A32
Author(s):  
F.J. Lin ◽  
F. Xie ◽  
Pickard
2017 ◽  
Vol 38 (3) ◽  
pp. 306-318 ◽  
Author(s):  
Brendan Mulhern ◽  
Richard Norman ◽  
Koonal Shah ◽  
Nick Bansback ◽  
Louise Longworth ◽  
...  

2016 ◽  
Vol 37 (3) ◽  
pp. 285-297 ◽  
Author(s):  
Brendan Mulhern ◽  
Nick Bansback ◽  
Arne Risa Hole ◽  
Aki Tsuchiya

Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing estimated utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs were assessed. Results: The zero prior value design produced a model with coefficients that were generally logically ordered, but the non-zero prior value design resulted in a set of less ordered coefficients where some differed significantly. The two-stage design resulted in ordered and significant coefficients. The non-zero prior value design may include more “difficult” choice sets, based on the proportions choosing each profile. Conclusions: There is some indication of compromised “respondent efficiency”, suggesting that the use of non-zero prior values will not necessarily result in better overall precision. It is feasible to design discrete choice experiments in two stages by allocating duration values to EQ-5D-5L health state pairs based on estimates from prior studies.


2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Helen J. Rogers ◽  
Zoe Marshman ◽  
Helen Rodd ◽  
Donna Rowen

Abstract Background Ordinal tasks are increasingly used to explore preferences for health states. This study aimed to determine the suitability of two ordinal preference elicitation tasks (discrete choice experiments (DCE) and best-worst scaling (BWS)) for use with children and young people to generate health state utility values. The study explored children’s understanding, the relationship between their age and level of understanding, and how many tasks they felt they could complete. Methods Children aged 11–16 years were recruited from a secondary school in South Yorkshire, UK. Participants were asked to ‘think aloud’ as they completed a computer-based survey that contained both DCE and BWS tasks relating to dental caries (tooth decay) health states. Health states involved descriptions of the impact of tooth decay on children’s daily lives. One-to-one semi-structured interviews were then held with participants, with use of a topic guide. Qualitative data were transcribed verbatim and analysed thematically. Results A total of 33 children (12 male, 21 female) participated, comprising 5–6 children from each school year group. Children expressed a preference for BWS and demonstrated a better understanding of these tasks than DCE. There was no clear relationship between children’s level of understanding and age. Children felt they could manage between 8 and 10 BWS tasks comfortably. Conclusion This study suggests that BWS tasks are the most appropriate type of preference elicitation task to value health states for children and young people aged 11–16 years to complete.


2016 ◽  
Vol 35 (4) ◽  
pp. 439-451 ◽  
Author(s):  
Brendan Mulhern ◽  
Richard Norman ◽  
Paula Lorgelly ◽  
Emily Lancsar ◽  
Julie Ratcliffe ◽  
...  

2012 ◽  
Vol 15 (7) ◽  
pp. A605-A606
Author(s):  
R. Norman ◽  
R. Viney ◽  
J. Brazier ◽  
L. Burgess ◽  
P. Cronin ◽  
...  

2017 ◽  
Vol 27 (12) ◽  
pp. 3544-3559 ◽  
Author(s):  
Anna Liza M Antonio ◽  
Robert E Weiss ◽  
Christopher S Saigal ◽  
Ely Dahan ◽  
Catherine M Crespi

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.


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