Using Discrete Choice Experiments with Duration to Model EQ-5D-5L Health State Preferences: Testing Experimental Design Strategies

Publication Type:
Journal Article
Citation:
Medical Decision Making, 2017, 37 (3), pp. 285 - 297
Issue Date:
2017-04-01
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© The Author(s) 2016. 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.
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