Literature DB >> 33451278

Differences in the Selection of Health State Utility Values by Sponsorship in Published Cost-Effectiveness Analyses.

Nathaniel Hendrix1, David D Kim2,3, Krishna S Patel4, Beth Devine1,4,5.   

Abstract

BACKGROUND: Health state utility values (HSUVs) are among the most influential attributes of cost-effectiveness analyses (CEAs). Our objective was to evaluate whether industry-funded studies select systematically different HSUVs as compared with studies without industry funding.
METHODS: Among 10 diseases with high disease burden in the United States, we further identified 31 progressive health states. We then searched the Tufts Medical Center's CEA Registry to identify studies that included HSUVs and were submitted to the registry between 2002 and 2019. Two reviewers mapped the free-text descriptions of health states onto the 31 predefined health states. We analyzed the effect of industry funding on the point estimates of these HSUVs with a beta regression. We also analyzed the difference between related health states within studies by funding source with a linear regression.
RESULTS: After identifying 26,222 HSUVs from 4198 CEAs, we matched 2573 HSUVs to the 31 predefined health states. We observed large variations within each health state: 12 of 31 health states included a range of HSUVs greater than 0.5. The point estimate model showed 1 statistically significant difference of 31 comparisons between studies with any industry funding and those without. The utility difference model found 3 significant differences out of 39 comparisons between CEAs with any industry funding and those without. LIMITATIONS: Inclusion of unpublished CEAs may have affected our conclusions about the effect of industry funding on selection of HSUVs. We also relied on free-text descriptions of health states available in the CEA Registry and did not include adjustment for multiple comparisons.
CONCLUSION: Limited evidence exists that industry-funded studies select different HSUVs compared to non-industry-funded studies for the health states we considered.

Entities:  

Keywords:  cost-effectiveness analysis; publications; regression analysis; utility theory

Year:  2021        PMID: 33451278      PMCID: PMC7987800          DOI: 10.1177/0272989X20985821

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  21 in total

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Authors:  Tessa Peasgood; Sue E Ward; John Brazier
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3.  A longitudinal comparison of 5 preference-weighted health state classification systems in persons with intervertebral disk herniation.

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Journal:  Med Decis Making       Date:  2010-11-22       Impact factor: 2.583

4.  Systematic searching and selection of health state utility values from the literature.

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Journal:  Value Health       Date:  2013-06       Impact factor: 5.725

Review 5.  Health utility bias: a systematic review and meta-analytic evaluation.

Authors:  Jason N Doctor; Han Bleichrodt; H Jill Lin
Journal:  Med Decis Making       Date:  2008-06-12       Impact factor: 2.583

6.  Industry involvement and baseline assumptions of cost-effectiveness analyses: diagnostic accuracy of the Papanicolaou test.

Authors:  Nikolaos P Polyzos; Antonis Valachis; Davide Mauri; John P A Ioannidis
Journal:  CMAJ       Date:  2011-03-14       Impact factor: 8.262

Review 7.  Are Current Reporting Standards Used to Describe Health State Utilities in Cost-Effectiveness Models Satisfactory?

Authors:  Roberta Ara; Harry Hill; Andrew Lloyd; Helen Buckley Woods; John Brazier
Journal:  Value Health       Date:  2020-02-15       Impact factor: 5.725

Review 8.  Comparison of direct and indirect methods of estimating health state utilities for resource allocation: review and empirical analysis.

Authors:  David Arnold; Alan Girling; Andrew Stevens; Richard Lilford
Journal:  BMJ       Date:  2009-07-22

9.  Comparison of health state utility estimates from instrument-based and vignette-based methods: a case study in kidney disease.

Authors:  Andrew H Briggs; Vasily Belozeroff; David Feeny
Journal:  BMC Res Notes       Date:  2019-07-08

10.  Global, regional, and national burden of Parkinson's disease, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

Authors: 
Journal:  Lancet Neurol       Date:  2018-10-01       Impact factor: 44.182

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  1 in total

1.  Industry sponsorship bias in cost effectiveness analysis: registry based analysis.

Authors:  Feng Xie; Ting Zhou
Journal:  BMJ       Date:  2022-06-22
  1 in total

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