Can we derive an 'exchange rate' between descriptive and preference-based outcome measures for stroke? Results from the transfer to utility (TTU) technique
Background Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a commo...
Saved in:
| Published in | Health and quality of life outcomes Vol. 7; no. 1; p. 33 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
17.04.2009
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1477-7525 1477-7525 |
| DOI | 10.1186/1477-7525-7-33 |
Cover
| Abstract | Background
Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when – because of timing, lack of foresight or cost considerations – only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke.
Methods
We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke.
Results
Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1–5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS ≥ 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale.
Conclusion
While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation. |
|---|---|
| AbstractList | Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when - because of timing, lack of foresight or cost considerations - only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke. We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke. Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1-5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS [greater than or equal to] 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale. While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation. Abstract Background Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when – because of timing, lack of foresight or cost considerations – only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke. Methods We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke. Results Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1–5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS ≥ 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale. Conclusion While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation. Background Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when – because of timing, lack of foresight or cost considerations – only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke. Methods We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke. Results Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1–5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS ≥ 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale. Conclusion While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation. Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when--because of timing, lack of foresight or cost considerations--only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke. We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke. Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1-5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS > or = 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale. While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation. Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when--because of timing, lack of foresight or cost considerations--only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke.BACKGROUNDStroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when--because of timing, lack of foresight or cost considerations--only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke.We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke.METHODSWe used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke.Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1-5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS > or = 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale.RESULTSTransformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1-5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS > or = 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale.While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation.CONCLUSIONWhile our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation. Background Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when - because of timing, lack of foresight or cost considerations - only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke. Methods We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke. Results Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1-5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS [greater than or equal to] 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale. Conclusion While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation. |
| ArticleNumber | 33 |
| Audience | Academic |
| Author | Mortimer, Duncan Sturm, Jonathan Segal, Leonie |
| AuthorAffiliation | 1 Centre for Health Economics, Monash University, Building 75, The Strip, Clayton 3800, Australia 3 Department of Neurology, Gosford Hospital, PO Box 361, New South Wales 2250, Australia 2 Division of Health Sciences, University of South Australia, Adelaide 5000, Australia |
| AuthorAffiliation_xml | – name: 3 Department of Neurology, Gosford Hospital, PO Box 361, New South Wales 2250, Australia – name: 2 Division of Health Sciences, University of South Australia, Adelaide 5000, Australia – name: 1 Centre for Health Economics, Monash University, Building 75, The Strip, Clayton 3800, Australia |
| Author_xml | – sequence: 1 givenname: Duncan surname: Mortimer fullname: Mortimer, Duncan email: duncan.mortimer@buseco.monash.edu.au organization: Centre for Health Economics, Monash University, Division of Health Sciences, University of South Australia – sequence: 2 givenname: Leonie surname: Segal fullname: Segal, Leonie organization: Division of Health Sciences, University of South Australia – sequence: 3 givenname: Jonathan surname: Sturm fullname: Sturm, Jonathan organization: Department of Neurology, Gosford Hospital |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19371444$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1ks1u1DAUhSNURNuBLUtkCYnCIq0dO3G8AVUVP5UqIaHp2vI41zMeEnuwnZY-Cy-Lw5S2g6iycHz8naNr6xwWe847KIqXBB8T0jYnhHFe8rqqS15S-qQ4uBP2HvzvF4cxrjGuaFXVz4p9IignjLGD4teZcugaUAfBXgHKmyP4qVfKLQEFleAILSBdA7hMRB3sJm2xDm0CGAjgNJQLFaFDfkzaD4AGUHEMEJHxAcUU_Hf4gL5BHPuUteAHlFaAUlAu5gCUPBqT7W26QW_n88t3KIFeOftjhOfFU6P6CC9u11lx-enj_OxLefH18_nZ6UWp64akUrQ1wdR0lDemFRxIp4RuNSGi40AVFVzpxmDBOt2QqhZVhggXTd0oDbQVdFacb3M7r9ZyE-ygwo30yso_gg9LqUKyugdZG4aZaVu8UMAWlChjGFXNIquMNHmMWfF-m7UZFwN0Gly-aL8Tunvi7Eou_ZWsmhYzPAW8uQ0IPr9BTHKwUUPfKwd-jLLhFWVc0Ay-3oJLlQezzvicpydYnhIh6hq3mGTq-D9U_joYrM5dMjbrO4ZXDy9wN_nfytwn6uBjzCW4R7CcOimn2smpdpJLOg3K_jFom1SyfnoA2z9uO9naYs7PdQxy7cfgchEec_wGPnjzZw |
| CitedBy_id | crossref_primary_10_1192_bjp_bp_113_136036 crossref_primary_10_1007_s40258_015_0191_4 crossref_primary_10_3111_13696998_2015_1070554 crossref_primary_10_1177_0272989X13500720 crossref_primary_10_3310_hta18270 crossref_primary_10_3109_15412555_2012_706666 crossref_primary_10_1017_S0266462315000379 crossref_primary_10_3310_hta24340 crossref_primary_10_1007_s11136_015_1082_8 crossref_primary_10_1007_s40273_015_0312_9 crossref_primary_10_1007_s40273_015_0319_2 crossref_primary_10_1177_0272989X15575285 crossref_primary_10_1177_0272989X10386800 crossref_primary_10_1016_j_jval_2012_12_006 crossref_primary_10_1016_j_jval_2016_11_026 crossref_primary_10_1007_s11136_023_03359_4 crossref_primary_10_1016_j_jval_2016_11_006 crossref_primary_10_1177_0272989X15598332 crossref_primary_10_47795_ZIWX7131 crossref_primary_10_1186_s12955_015_0305_6 crossref_primary_10_1186_s12955_018_0857_3 crossref_primary_10_1007_s40258_019_00467_6 crossref_primary_10_1111_j_1365_2702_2012_04246_x crossref_primary_10_1007_s11136_012_0207_6 |
| Cites_doi | 10.1111/j.1524-4733.2007.00194.x 10.1016/S0167-6296(01)00130-8 10.1177/0272989X07309642 10.1186/1477-7525-3-11 10.1177/0272989X9901900203 10.1177/0272989X03261562 10.1177/0272989X9701700101 10.1161/01.STR.31.9.2087 10.1016/S0895-4356(98)00103-6 10.2165/00003495-200565040-00004 10.1177/02729890122062352 10.1207/S15328031US0104_02 10.1161/01.STR.0000110984.91157.BD 10.3109/07853890109002090 10.1161/01.STR.0000040407.44712.C7 10.1111/j.1467-842X.2005.tb00063.x 10.1097/01.mlr.0000135827.18610.0d 10.1093/oso/9780195108248.001.0001 10.1002/hec.789 10.1023/A:1008815005736 10.1161/01.STR.20.7.864 10.1016/0277-9536(94)90162-7 10.1097/00007632-200006150-00002 10.1146/annurev.publhealth.21.1.587 10.1177/0272989X04264015 10.1177/0272989X04265477 10.1097/01.MLR.0000093480.58308.D8 10.5694/j.1326-5377.2004.tb05907.x |
| ContentType | Journal Article |
| Copyright | Mortimer et al; licensee BioMed Central Ltd. 2009 COPYRIGHT 2009 BioMed Central Ltd. Copyright © 2009 Mortimer et al; licensee BioMed Central Ltd. 2009 Mortimer et al; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Mortimer et al; licensee BioMed Central Ltd. 2009 – notice: COPYRIGHT 2009 BioMed Central Ltd. – notice: Copyright © 2009 Mortimer et al; licensee BioMed Central Ltd. 2009 Mortimer et al; licensee BioMed Central Ltd. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM DOA |
| DOI | 10.1186/1477-7525-7-33 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Public Health |
| EISSN | 1477-7525 |
| EndPage | 33 |
| ExternalDocumentID | oai_doaj_org_article_5f404f880bae4b31aff43a6b40441610 PMC2680400 A199550801 19371444 10_1186_1477_7525_7_33 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GeographicLocations | Victoria Australia |
| GeographicLocations_xml | – name: Victoria – name: Australia |
| GroupedDBID | --- 0R~ 29I 2VQ 2WC 4.4 44B 53G 5VS 7X7 7XC 88E 8C1 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABUWG ACGFO ACGFS ACHQT ACPRK ACUHS ADBBV ADRAZ ADUKV AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ATCPS BAPOH BAWUL BCNDV BENPR BFQNJ BHPHI BMC BPHCQ BVXVI C1A C6C CCPQU CS3 DIK DU5 E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P FYUFA GROUPED_DOAJ GX1 H13 HCIFZ HMCUK HYE IAO IHR INH INR IPNFZ IPY ISR ITC KQ8 M1P M48 M~E O5R O5S OK1 OVT P2P PATMY PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO PUEGO PYCSY RBZ RIG RNS ROL RPM RSV SMD SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX ALIPV CITATION CGR CUY CVF ECM EIF NPM PMFND 7X8 5PM |
| ID | FETCH-LOGICAL-c561t-985103fd376f897e1da9c8c119d7e3a397ac6f094dc612592897179656ace3893 |
| IEDL.DBID | M48 |
| ISSN | 1477-7525 |
| IngestDate | Wed Aug 27 01:29:22 EDT 2025 Thu Aug 21 18:27:53 EDT 2025 Thu Sep 04 17:51:52 EDT 2025 Tue Jun 17 22:08:22 EDT 2025 Tue Jun 10 21:05:16 EDT 2025 Mon Jul 21 05:15:58 EDT 2025 Tue Jul 01 04:06:27 EDT 2025 Thu Apr 24 23:11:36 EDT 2025 Sat Sep 06 07:28:35 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Conversion Algorithm Predictive Validity Descriptive Measure Hausman Specification Test North East Melbourne Stroke Incidence |
| Language | English |
| License | http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c561t-985103fd376f897e1da9c8c119d7e3a397ac6f094dc612592897179656ace3893 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://doi.org/10.1186/1477-7525-7-33 |
| PMID | 19371444 |
| PQID | 67234793 |
| PQPubID | 23479 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_5f404f880bae4b31aff43a6b40441610 pubmedcentral_primary_oai_pubmedcentral_nih_gov_2680400 proquest_miscellaneous_67234793 gale_infotracmisc_A199550801 gale_infotracacademiconefile_A199550801 pubmed_primary_19371444 crossref_primary_10_1186_1477_7525_7_33 crossref_citationtrail_10_1186_1477_7525_7_33 springer_journals_10_1186_1477_7525_7_33 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2009-04-17 |
| PublicationDateYYYYMMDD | 2009-04-17 |
| PublicationDate_xml | – month: 04 year: 2009 text: 2009-04-17 day: 17 |
| PublicationDecade | 2000 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Health and quality of life outcomes |
| PublicationTitleAbbrev | Health Qual Life Outcomes |
| PublicationTitleAlternate | Health Qual Life Outcomes |
| PublicationYear | 2009 |
| Publisher | BioMed Central BioMed Central Ltd BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: BMC |
| References | DG Fryback (550_CR7) 1997; 17 L Lundberg (550_CR32) 1999; 19 D Mortimer (550_CR10) 2007; 10 L Segal (550_CR5) 2004; 180 AC Harvey (550_CR22) 1981 550_CR25 550_CR24 J Ware (550_CR16) 2000; 25 WF Lawrence (550_CR8) 2004; 24 JW Sturm (550_CR15) 2002; 33 P Franks (550_CR31) 2003; 41 A Gelman (550_CR37) 2007 J Brazier (550_CR29) 2002; 21 RM Kaplan (550_CR27) 2002 A Pickard (550_CR35) 2004; 35 PJ Neumann (550_CR4) 2000; 21 G Hawthorne (550_CR26) 2005; 29 A Shmueli (550_CR34) 2004; 24 M Gold (550_CR2) 1996 F Mahoney (550_CR19) 1965; 14 H Goldstein (550_CR21) 2002; 1 P Franks (550_CR6) 2004; 24 AS Pickard (550_CR28) 2005; 3 J Ware (550_CR17) 1994 AG Thrift (550_CR11) 2000; 31 J Brazier (550_CR36) 1998; 51 WH Greene (550_CR20) 1993 J Richardson (550_CR1) 1994; 39 BJ O'Brien (550_CR23) 2003; 12 MB Nichol (550_CR33) 2001; 21 NJ Bansback (550_CR3) 2005; 65 S Hatano (550_CR12) 1976; 54 T Brott (550_CR18) 1989; 20 G Hawthorne (550_CR13) 2001; 33 D Mortimer (550_CR9) 2008; 28 G Hawthorne (550_CR14) 1999; 8 J Brazier (550_CR30) 2004; 42 8066489 - Soc Sci Med. 1994 Jul;39(1):7-21 18263562 - Med Decis Making. 2008 Jan-Feb;28(1):66-89 15319610 - Med Care. 2004 Sep;42(9):851-9 17888105 - Value Health. 2007 Sep-Oct;10(5):398-407 15748287 - Health Qual Life Outcomes. 2005;3:11 15090102 - Med Decis Making. 2004 Mar-Apr;24(2):160-9 14984357 - Med J Aust. 2004 Mar 1;180(5 Suppl):S11-7 15915617 - Aust N Z J Public Health. 2005 Apr;29(2):136-42 10884966 - Annu Rev Public Health. 2000;21:587-611 15005955 - Med Decis Making. 2004 Jan-Feb;24(1):61-3 1088404 - Bull World Health Organ. 1976;54(5):541-53 10231075 - Med Decis Making. 1999 Apr-Jun;19(2):128-40 14726549 - Stroke. 2004 Feb;35(2):607-12 11491195 - Ann Med. 2001 Jul;33(5):358-70 14583690 - Med Care. 2003 Nov;41(11):1277-83 11310943 - Med Decis Making. 2001 Mar-Apr;21(2):105-12 11939242 - J Health Econ. 2002 Mar;21(2):271-92 12468787 - Stroke. 2002 Dec;33(12):2888-94 9817129 - J Clin Epidemiol. 1998 Nov;51(11):1115-28 15733011 - Drugs. 2005;65(4):473-96 10851092 - Spine (Phila Pa 1976). 2000 Jun 15;25(12):1467 15185716 - Med Decis Making. 2004 May-Jun;24(3):247-54 10472152 - Qual Life Res. 1999 May;8(3):209-24 2749846 - Stroke. 1989 Jul;20(7):864-70 14601159 - Health Econ. 2003 Nov;12(11):975-81 8994146 - Med Decis Making. 1997 Jan-Mar;17(1):1-9 14258950 - Md State Med J. 1965 Feb;14:61-5 10978034 - Stroke. 2000 Sep;31(9):2087-92 |
| References_xml | – volume: 10 start-page: 398 issue: 5 year: 2007 ident: 550_CR10 publication-title: Value in Health doi: 10.1111/j.1524-4733.2007.00194.x – ident: 550_CR25 – volume: 21 start-page: 271 year: 2002 ident: 550_CR29 publication-title: Journal of Health Economics doi: 10.1016/S0167-6296(01)00130-8 – volume: 28 start-page: 66 year: 2008 ident: 550_CR9 publication-title: Medical Decision Making doi: 10.1177/0272989X07309642 – volume: 3 start-page: 11 issue: 1 year: 2005 ident: 550_CR28 publication-title: Health & Quality of Life Outcomes doi: 10.1186/1477-7525-3-11 – volume-title: Presented at 9th Annual Conference of the International Society for Quality of Life Research (ISOQOL) year: 2002 ident: 550_CR27 – volume: 19 start-page: 128 year: 1999 ident: 550_CR32 publication-title: Medical Decision Making doi: 10.1177/0272989X9901900203 – volume: 24 start-page: 61 year: 2004 ident: 550_CR34 publication-title: Medical Decision Making doi: 10.1177/0272989X03261562 – volume: 54 start-page: 541 year: 1976 ident: 550_CR12 publication-title: Bulletin of the World Health Organization – volume: 17 start-page: 1 year: 1997 ident: 550_CR7 publication-title: Medical Decision Making doi: 10.1177/0272989X9701700101 – volume: 31 start-page: 2087 year: 2000 ident: 550_CR11 publication-title: Stroke doi: 10.1161/01.STR.31.9.2087 – volume: 51 start-page: 1115 year: 1998 ident: 550_CR36 publication-title: Journal of Clinical Epidemiology doi: 10.1016/S0895-4356(98)00103-6 – volume: 65 start-page: 473 year: 2005 ident: 550_CR3 publication-title: Drugs doi: 10.2165/00003495-200565040-00004 – volume-title: SF-36 physical and mental health summary scales: A user's manual year: 1994 ident: 550_CR17 – volume: 21 start-page: 105 year: 2001 ident: 550_CR33 publication-title: Medical Decision Making doi: 10.1177/02729890122062352 – volume: 1 start-page: 223 year: 2002 ident: 550_CR21 publication-title: Understanding Statistics doi: 10.1207/S15328031US0104_02 – volume: 35 start-page: 607 year: 2004 ident: 550_CR35 publication-title: Stroke doi: 10.1161/01.STR.0000110984.91157.BD – volume: 33 start-page: 358 year: 2001 ident: 550_CR13 publication-title: Annals of Medicine doi: 10.3109/07853890109002090 – volume: 33 start-page: 2888 year: 2002 ident: 550_CR15 publication-title: Stroke doi: 10.1161/01.STR.0000040407.44712.C7 – volume: 29 start-page: 136 issue: 2 year: 2005 ident: 550_CR26 publication-title: Australian and New Zealand Journal of Public Health doi: 10.1111/j.1467-842X.2005.tb00063.x – ident: 550_CR24 – volume-title: Econometric Analysis year: 1993 ident: 550_CR20 – volume: 42 start-page: 851 issue: 9 year: 2004 ident: 550_CR30 publication-title: Medical Care doi: 10.1097/01.mlr.0000135827.18610.0d – volume-title: Cost-effectiveness in health and medicine year: 1996 ident: 550_CR2 doi: 10.1093/oso/9780195108248.001.0001 – volume: 12 start-page: 975 issue: 11 year: 2003 ident: 550_CR23 publication-title: Health Economics doi: 10.1002/hec.789 – volume-title: Data Analysis Using Regression and Multilevel/Hierarchical Models year: 2007 ident: 550_CR37 – volume: 8 start-page: 209 year: 1999 ident: 550_CR14 publication-title: Quality of Life Research doi: 10.1023/A:1008815005736 – volume: 14 start-page: 61 year: 1965 ident: 550_CR19 publication-title: Maryland Medical Journal – volume: 20 start-page: 864 year: 1989 ident: 550_CR18 publication-title: Stroke doi: 10.1161/01.STR.20.7.864 – volume: 39 start-page: 7 year: 1994 ident: 550_CR1 publication-title: Social Science and Medicine doi: 10.1016/0277-9536(94)90162-7 – volume: 25 start-page: 1467 year: 2000 ident: 550_CR16 publication-title: Spine doi: 10.1097/00007632-200006150-00002 – volume-title: The Econometric Analysis of Time Series year: 1981 ident: 550_CR22 – volume: 21 start-page: 587 year: 2000 ident: 550_CR4 publication-title: Annual Review of Public Health doi: 10.1146/annurev.publhealth.21.1.587 – volume: 24 start-page: 160 year: 2004 ident: 550_CR8 publication-title: Medical Decision Making doi: 10.1177/0272989X04264015 – volume: 24 start-page: 247 year: 2004 ident: 550_CR6 publication-title: Medical Decision Making doi: 10.1177/0272989X04265477 – volume: 41 start-page: 1277 issue: 11 year: 2003 ident: 550_CR31 publication-title: Medical Care doi: 10.1097/01.MLR.0000093480.58308.D8 – volume: 180 start-page: S11 year: 2004 ident: 550_CR5 publication-title: Medical Journal of Australia doi: 10.5694/j.1326-5377.2004.tb05907.x – reference: 14258950 - Md State Med J. 1965 Feb;14:61-5 – reference: 8066489 - Soc Sci Med. 1994 Jul;39(1):7-21 – reference: 12468787 - Stroke. 2002 Dec;33(12):2888-94 – reference: 11491195 - Ann Med. 2001 Jul;33(5):358-70 – reference: 15915617 - Aust N Z J Public Health. 2005 Apr;29(2):136-42 – reference: 10978034 - Stroke. 2000 Sep;31(9):2087-92 – reference: 17888105 - Value Health. 2007 Sep-Oct;10(5):398-407 – reference: 14601159 - Health Econ. 2003 Nov;12(11):975-81 – reference: 15005955 - Med Decis Making. 2004 Jan-Feb;24(1):61-3 – reference: 2749846 - Stroke. 1989 Jul;20(7):864-70 – reference: 9817129 - J Clin Epidemiol. 1998 Nov;51(11):1115-28 – reference: 10231075 - Med Decis Making. 1999 Apr-Jun;19(2):128-40 – reference: 18263562 - Med Decis Making. 2008 Jan-Feb;28(1):66-89 – reference: 8994146 - Med Decis Making. 1997 Jan-Mar;17(1):1-9 – reference: 15748287 - Health Qual Life Outcomes. 2005;3:11 – reference: 10851092 - Spine (Phila Pa 1976). 2000 Jun 15;25(12):1467 – reference: 11939242 - J Health Econ. 2002 Mar;21(2):271-92 – reference: 14583690 - Med Care. 2003 Nov;41(11):1277-83 – reference: 10884966 - Annu Rev Public Health. 2000;21:587-611 – reference: 14726549 - Stroke. 2004 Feb;35(2):607-12 – reference: 11310943 - Med Decis Making. 2001 Mar-Apr;21(2):105-12 – reference: 15319610 - Med Care. 2004 Sep;42(9):851-9 – reference: 10472152 - Qual Life Res. 1999 May;8(3):209-24 – reference: 15185716 - Med Decis Making. 2004 May-Jun;24(3):247-54 – reference: 1088404 - Bull World Health Organ. 1976;54(5):541-53 – reference: 15090102 - Med Decis Making. 2004 Mar-Apr;24(2):160-9 – reference: 15733011 - Drugs. 2005;65(4):473-96 – reference: 14984357 - Med J Aust. 2004 Mar 1;180(5 Suppl):S11-7 |
| SSID | ssj0023225 |
| Score | 2.0151067 |
| Snippet | Background
Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of... Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader... Background Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of... Abstract Background Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 33 |
| SubjectTerms | Aged Aged, 80 and over Australia Care and treatment Evaluation Female Health Status Humans Male Medicine Medicine & Public Health Methods Middle Aged Mortality Outcome Assessment (Health Care) - methods Patient outcomes Patient Satisfaction Quality of Life Quality of Life Research Quality-Adjusted Life Years Regression Analysis Stroke Stroke (Disease) Surveys and Questionnaires Victoria |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwELVQT0gI8U1KgTkgbTlYTRpvnJwQVFQVEhzQrtSb5ThjgWiTapNt-S_8WWacD5qiigvHtUfK2n6eeZOMn4V4g5lH5XUmWUpEKhs7ycpKskJtib9764KI6-cv2clafTpdnl676otrwnp54H7iDpZexcoTykqLqkwT671KbVZSK3PzkK3HRTwmU0OqxTAN54q0lnp5uBzkGpM8O5jaJO2udBaOgmr_3775WnC6WTh54-tpCErHD8T9gU3C-34UD8UdrB-Je_2rOOhPGD0Wv45sDVcIFYHtEoF-LPBnf-AXWChiAUOxFlkMTiSYVXAxXUIiOdpV0Gw7QijCef9msQWivNB2m-YHvoOv2G7POmrbNOdAvBK6QIpxA10DhG8m_LC_Wq3fwqQc-0Ssjz-ujk7kcCeDdMS0OlnkLMHnK_JLPi80JpUtXO6SpKg0ppbYDa2up5yxcsydCsrnKGEsiDVah0yOnoqduqnxuQDqjlHFvnSVVZgty5JLrtKMYmaJSV5EQo5LY9wgWM73ZpyZkLjkmeGlNLyURps0jcRisr_opTputfzAKz1ZscR2aCDgmQF45l_Ao8cxTgw7Avpbzg7nGWhwLKllWE-Y0j8aTST2Zpa0gd2s-_WINMNdXPVWY7NtTaYPw0HfSDzrcfdnXCxjqJSKhJ4hcjakeU_9_VtQD6c9yY47Evsjds3gttpbpmv3f0zXC3F3_BCX6D2x0222-JL4XFe-Clv3N_2ARFI priority: 102 providerName: Directory of Open Access Journals – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Nb9QwELWgSAgJIShfgQI-IG05WGyIYycnBCuqCqkc0K7Um-U4Y4Fok2qTbfkv_FlmHG9oWlXiuPasdp15Hr-Jx8-MvQXlQXqtBEmJCGnnTpCykqhBW-Tv3rog4nr0TR2u5Nfj_DgWyNJZmMv792mh3qdSa6FzunNVZNltdifHoEtIXqjFmFgRKKMg4_XvTBacoMt_PfpeWn6ulkZe2R8Ny87BQ_Yg8kX-aXDwI3YLml129yjuiO-y-8N7Nz4cJ3rM_ixswy-A14isc-D4YQa_h9O9nFQhZjxWZqFFjBjBrOZn440jgpa2mrebHuEI_HR4jdhx5Le869ftL_jIv0O3Oemxbd2eciSRvA8MGNa8bzmCmdg9318uV-_4KBP7hK0OviwXhyJewCAc0qpelAXp7fkag5AvSg1pbUtXuDQtaw2ZRSqDrvSYINaOiFKJyRtmhyVSROuAmNBTttO0DTxnHLvnIOe-crWVoPKqovqqTOECWUFalAkTWy8ZF9XJ6ZKMExOylEIZ8qohrxptsixhs9H-bNDluNHyMzl9tCI97dCAMDNxeprcy7n0GMsqC7LKUuu9zKyqsJUywDn-HEHG0KzHv-VsPLyAgyP9LEPiwZjr4WgStjexxNnqJt1vtqAz1EUlbg20m84o_SGc6k3YswGC_8ZFmoVSyoTpCTgnQ5r2ND9_BKlwnIAUpRO2v4WxiTGqu-Fxvfh_05fs3nZvLdV7bKdfb-AVUrS-eh3m518f8jRy priority: 102 providerName: Springer Nature |
| Title | Can we derive an 'exchange rate' between descriptive and preference-based outcome measures for stroke? Results from the transfer to utility (TTU) technique |
| URI | https://link.springer.com/article/10.1186/1477-7525-7-33 https://www.ncbi.nlm.nih.gov/pubmed/19371444 https://www.proquest.com/docview/67234793 https://pubmed.ncbi.nlm.nih.gov/PMC2680400 https://doaj.org/article/5f404f880bae4b31aff43a6b40441610 |
| Volume | 7 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3fb9MwELbY9oKEEL8JjOIHpI4HQ7O4cfKAJlptmpCYUNVKFS-W45wHoktGmsL4W_hnuXPSjmxMvERKfFXj-Dv7O_v8mbFXEDuQTsWCpESENAMrSFlJ5KAM8ndnrBdx_XgSH8_kh_lwfpn_1H7A5T9DOzpPalYt3lx8_3WADv_OO3wSvw2lUkIN6VxWEUVbbMevFVEan9ysKOwTcP1Oo9a2FXC8_vvOAOV1_K_31n8NV1dTKa-sp_ph6ugeu9vyS_6-AcR9dguKB-xOMznHmz1HD9nvsSn4T-A5wu8HcLzpw0WzBZiTdESft-lbaNF2K94s5-ebY0kEjX85L1c1Yhb4WTPXuORIgvmyrspvcMAnsFwtanxWlWccmSavPU2GitclR8RTCMD3ptPZa77Rkn3EZkeH0_GxaE9pEBa5Vy3ShET5XI49lUtSBWFuUpvYMExzBZFBvoPt7TCKzC2xqRQjPAwhU-SRxgLRpcdsuygLeMo4Fg9ADlxmcyMhHmYZJWFFMY6iGYRJGjCxbhptWwlzOkljoX0ok8SamlJTU2qloyhg_Y39eSPecaPliFp6Y0Wi2_5BWZ3q1of10MmBdNjhZQZkFoXGORmZOMOnFCYO8O8IJ5rAiq9lTbvDAStHIluaFIYxIMTaBGy3Y4kubTvFL9dI01REeXAFlKuljtW-3_obsCcN7i7rRcKGUsqAqQ4iO1XqlhRfv3g9cfRS6soDtrfGrl774Q2f69l_X_A5u71edwvVLtuuqxW8QPpWZz22peYKr8k47LGd0eHJpwnejeNxz0-I9Lzf4nUy-vwH-CtGQg |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fb9MwELZgSICEEAwYgcH8gNTxYC1Z3Dh5QlAxFVj3gFppb5bjnAViS6Y2Bf4X_lnuHDcsmybxWPuq1rkf_i6--8zYG8gcSKcyQVQiQprYCmJWEhUog_jdGetJXGcn2XQhP5-OT0OBLPXCXD6_T_LsIJFKCTWmO1dFmt5md-jUkjjyJ9mkT6zIKAMh4_XvDDYcz8t_Pfpe2n6ulkZeOR_1287RI_Yw4EX-vlPwY3YL6m12dxZOxLfZg-69G-_aiZ6wPxNT81_AK7Ssn8Dxwwh-d929nFghRjxUZqFEiBherOIX_Y0jgra2ijfrFs0R-Hn3GnHFEd_yVbtsfsA7_hVW67MWx5bNOUcQyVuPgGHJ24ajMRO65_vz-eIt72lin7LF0cf5ZCrCBQzCIqxqRZET356rMAi5vFCQVKawuU2SolKQGoQyqEqHCWJlCSgVmLxhdlggRDQWCAk9Y1t1U8NzxnE6Bhm70lZGQjYuS6qvSjPcIEtI8iJiYqMlbQM7OV2ScaZ9lpJnmrSqSata6TSN2KiXv-h4OW6U_EBK76WIT9sPoJnp4J567GQsHcay0oAs08Q4J1OTlThKGWCMP0cmo8nr8W9ZE5oXcHHEn6WJPBhzPVxNxHYHkuitdjC9tzE6TVNU4lZDs17pTB36rt6I7XQm-G9dxFkopYyYGhjnYEnDmfr7N08Vjg5IUTpi-xsz1iFGrW54XC_-X3SP3ZvOZ8f6-NPJl5fs_uacLVG7bKtdruEVwrW2fO199S_J7zdh |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fb9MwED6NTZqQEIIBIzCYH5A6HqwlxI0TCQmVQTUKmxC00t4sx7EBsSVVmwL_C_8sd4lTyKZJPNa-qnXuh7-L7z4DPLOJs8LJhBOVCBc6NJyYlXhhpUb87rRpSFxPTpPjmZicDc824GXXC9NUu3dHkm1PA7E0lfXhvHCti6fJYSSk5HJIN7HyOL4BW-kwyzD12hqNJp8n64SLjNUTNV79Vm8javj6r0blf7alyyWTl85Nm-1ofAduexzJRq3i78KGLXdg-8SflO_ArfZ9HGvbjO7B7yNdsp-WFWhxPyzDDwP7q-36ZcQWMWC-YgslfCRpxAo2X99EwmnLK1i1qvGJWXbRvl5cMsS9bFkvqu_2Fftkl6vzGscW1QVDcMnqBhnbBasrhkZOqJ8dTKez52xNH3sfZuO306Nj7i9m4AbhVs2zlHj4XIHByaWZtFGhM5OaKMoKaWONEAdV7DBxLAwBqAyTOswaM4SO2lhCSA9gs6xK-xAYTodWhC43hRY2GeY51V3FCW6cuY3SLADeaUkZz1pOl2ecqyZ7SRNFWlWkVSVVHAcwWMvPW76OayVfk9LXUsSz3QxUiy_Ku60aOhEKhzEu11bkcaSdE7FOchylzDDEnyOTURQN8G8Z7ZsacHHEq6WIVBhzQFxNAHs9SfRi05ve74xO0RSVvpW2Wi1VIl803b4B7LYm-HddxGUohAhA9oyzt6T-TPnta0Mhjo5J0TuAg86MlY9dy2se16P_F92H7Y9vxurDu9P3j-Fmd_wWyT3YrBcr-wRRXJ0_9c76Bxi0QR8 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Can+we+derive+an+%27exchange+rate%27+between+descriptive+and+preference-based+outcome+measures+for+stroke%3F+Results+from+the+transfer+to+utility+%28TTU%29+technique&rft.jtitle=Health+and+quality+of+life+outcomes&rft.au=Mortimer%2C+Duncan&rft.au=Segal%2C+Leonie&rft.au=Sturm%2C+Jonathan&rft.date=2009-04-17&rft.issn=1477-7525&rft.eissn=1477-7525&rft.volume=7&rft.spage=33&rft_id=info:doi/10.1186%2F1477-7525-7-33&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1477-7525&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1477-7525&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1477-7525&client=summon |