Is dimension order important when valuing health states using Discrete Choice Experiments including duration?
Background: Discrete choice experiments with duration (DCETTO) can be used to estimate utility values for preference-based measures, such as the EQ-5D-5L. For self-completion, the health dimensions are presented in a standard order. However, for valuation, this may result in order effects. Thus, it...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
Springer
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/35471 |
| _version_ | 1848754505823289344 |
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| author | Mulhern, B. Norman, Richard Lorgelly, P. Lancsar, E. Ratcliffe, J. Brazier, J. Viney, R. |
| author_facet | Mulhern, B. Norman, Richard Lorgelly, P. Lancsar, E. Ratcliffe, J. Brazier, J. Viney, R. |
| author_sort | Mulhern, B. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Background: Discrete choice experiments with duration (DCETTO) can be used to estimate utility values for preference-based measures, such as the EQ-5D-5L. For self-completion, the health dimensions are presented in a standard order. However, for valuation, this may result in order effects. Thus, it is important to understand whether health state dimension ordering affects values. The aim of this study was to examine the importance of dimension ordering on DCE values using EQ-5D-5L. Methods: A choice experiment presenting two health profiles and a third immediate death option was developed. A three-arm study was used, with the same 120 choice sets presented online across each arm (n = 360 per arm). Arm 1 presented the standard EQ-5D-5L dimension order, arm 2 randomised order between respondents, and arm 3 randomised within respondents. Conditional logit regression was used to assess model consistency, and scale parameter testing was used to assess model poolability. Results: There were minor inconsistencies across each arm, but the magnitudes of the coefficients produced were generally consistent. Arm 3 produced the largest range of utility values (1 to −0.980). Scale parameter testing suggested that the models did not differ, and the data could be pooled. Follow-up questions did not suggest variation in terms of difficulty. Conclusions: The results suggest that the level of randomisation used in DCE health state valuation studies does not significantly impact values, and dimension order may not be as important as other study design issues. The results support past valuation studies that use the standard order of dimensions. |
| first_indexed | 2025-11-14T08:41:29Z |
| format | Journal Article |
| id | curtin-20.500.11937-35471 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:41:29Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-354712019-09-02T07:34:03Z Is dimension order important when valuing health states using Discrete Choice Experiments including duration? Mulhern, B. Norman, Richard Lorgelly, P. Lancsar, E. Ratcliffe, J. Brazier, J. Viney, R. Background: Discrete choice experiments with duration (DCETTO) can be used to estimate utility values for preference-based measures, such as the EQ-5D-5L. For self-completion, the health dimensions are presented in a standard order. However, for valuation, this may result in order effects. Thus, it is important to understand whether health state dimension ordering affects values. The aim of this study was to examine the importance of dimension ordering on DCE values using EQ-5D-5L. Methods: A choice experiment presenting two health profiles and a third immediate death option was developed. A three-arm study was used, with the same 120 choice sets presented online across each arm (n = 360 per arm). Arm 1 presented the standard EQ-5D-5L dimension order, arm 2 randomised order between respondents, and arm 3 randomised within respondents. Conditional logit regression was used to assess model consistency, and scale parameter testing was used to assess model poolability. Results: There were minor inconsistencies across each arm, but the magnitudes of the coefficients produced were generally consistent. Arm 3 produced the largest range of utility values (1 to −0.980). Scale parameter testing suggested that the models did not differ, and the data could be pooled. Follow-up questions did not suggest variation in terms of difficulty. Conclusions: The results suggest that the level of randomisation used in DCE health state valuation studies does not significantly impact values, and dimension order may not be as important as other study design issues. The results support past valuation studies that use the standard order of dimensions. 2017 Journal Article http://hdl.handle.net/20.500.11937/35471 10.1007/s40273-016-0475-z Springer fulltext |
| spellingShingle | Mulhern, B. Norman, Richard Lorgelly, P. Lancsar, E. Ratcliffe, J. Brazier, J. Viney, R. Is dimension order important when valuing health states using Discrete Choice Experiments including duration? |
| title | Is dimension order important when valuing health states using Discrete Choice Experiments including duration? |
| title_full | Is dimension order important when valuing health states using Discrete Choice Experiments including duration? |
| title_fullStr | Is dimension order important when valuing health states using Discrete Choice Experiments including duration? |
| title_full_unstemmed | Is dimension order important when valuing health states using Discrete Choice Experiments including duration? |
| title_short | Is dimension order important when valuing health states using Discrete Choice Experiments including duration? |
| title_sort | is dimension order important when valuing health states using discrete choice experiments including duration? |
| url | http://hdl.handle.net/20.500.11937/35471 |