Literature DB >> 18057188

Predicting utility ratings for joint health States from single health States in prostate cancer: empirical testing of 3 alternative theories.

William Dale1, Anirban Basu, Arthur Elstein, David Meltzer.   

Abstract

BACKGROUND: Cost-effectiveness analyses measure quality of life by associating utilities with specific health states. Utilities are often defined by single health states, such as incontinence or impotence in the case of prostate cancer treatments. Health conditions often occur simultaneously, yielding joint health states (e.g., impotence with incontinence). Given the combinatorial mathematics involved, even a small number of conditions can result in large numbers of potential joint states, complicating utility elicitation for all relevant states. Analytic predictions for joint-state utilities have been based on 3 theoretical models: 1) multiplicative, 2) additive, and 3) minimum models. These models' empirical accuracy for joint-state utility prediction has been minimally examined. The authors compared these 3 models for predicting joint-state utilities from single-state utilities in men at the time of prostate biopsies.
METHODS: Utilities were collected using time tradeoff in 2 university-based prostate biopsy clinics (N = 147). Single-state utilities were elicited for impotence, incontinence, watchful waiting, and post-prostatectomy. Joint-state utilities were elicited for states combining impotence with 1) incontinence, 2) postprostatectomy, or 3) watchful waiting. Testing 3 prediction models of joint-state utilities for bias and consistency, the predictions were compared against directly elicited joint-state utilities.
RESULTS: All 3 models are biased. The minimum model is preferred, being the least biased and most efficient.
CONCLUSIONS: No current model accurately predicts joint-state utility using the component single-state utilities. When possible, joint-state utilities should be elicited. If not possible, the minimum model is recommended. Research to identify better models is needed.

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Year:  2007        PMID: 18057188     DOI: 10.1177/0272989X07309639

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


  16 in total

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Authors:  Katerina Papageorgiou; Karin M Vermeulen; Fenna R M Leijten; Erik Buskens; Adelita V Ranchor; Maya J Schroevers
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2.  Sourcing quality-of-life weights obtained from previous studies: theory and reality in Korea.

Authors:  SeungJin Bae; Eun Young Bae; Sang Hee Lim
Journal:  Patient       Date:  2014       Impact factor: 3.883

3.  Active surveillance compared with initial treatment for men with low-risk prostate cancer: a decision analysis.

Authors:  Julia H Hayes; Daniel A Ollendorf; Steven D Pearson; Michael J Barry; Philip W Kantoff; Susan T Stewart; Vibha Bhatnagar; Christopher J Sweeney; James E Stahl; Pamela M McMahon
Journal:  JAMA       Date:  2010-12-01       Impact factor: 56.272

Review 4.  PSA-based prostate cancer screening: the role of active surveillance and informed and shared decision making.

Authors:  Lionne D F Venderbos; Monique J Roobol
Journal:  Asian J Androl       Date:  2011-02-07       Impact factor: 3.285

5.  Population preference values for treatment outcomes in chronic lymphocytic leukaemia: a cross-sectional utility study.

Authors:  Kathleen M Beusterien; John Davies; Michael Leach; David Meiklejohn; Jessica L Grinspan; Alison O'Toole; Steve Bramham-Jones
Journal:  Health Qual Life Outcomes       Date:  2010-05-18       Impact factor: 3.186

6.  Comparative effectiveness research for antipsychotic medications: how much is enough?

Authors:  David O Meltzer; Anirban Basu; Herbert Y Meltzer
Journal:  Health Aff (Millwood)       Date:  2009-07-21       Impact factor: 6.301

7.  Observation versus initial treatment for men with localized, low-risk prostate cancer: a cost-effectiveness analysis.

Authors:  Julia H Hayes; Daniel A Ollendorf; Steven D Pearson; Michael J Barry; Philip W Kantoff; Pablo A Lee; Pamela M McMahon
Journal:  Ann Intern Med       Date:  2013-06-18       Impact factor: 25.391

8.  Three methods tested to model SF-6D health utilities for health states involving comorbidity/co-occurring conditions.

Authors:  Janel Hanmer; David Vanness; Ronald Gangnon; Mari Palta; Dennis G Fryback
Journal:  J Clin Epidemiol       Date:  2009-11-06       Impact factor: 6.437

9.  Health-related quality of life of alcohol use disorder with co-occurring conditions in the US population.

Authors:  Eve Wittenberg; Carolina Barbosa; Riley Hein; Emma Hudson; Benjamin Thornburg; Jeremy W Bray
Journal:  Drug Alcohol Depend       Date:  2021-01-29       Impact factor: 4.492

10.  Societal preference values for advanced melanoma health states in the United Kingdom and Australia.

Authors:  K M Beusterien; S M Szabo; S Kotapati; J Mukherjee; A Hoos; P Hersey; M R Middleton; A R Levy
Journal:  Br J Cancer       Date:  2009-07-14       Impact factor: 7.640

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