Literature DB >> 9161116

Constructing health state preference values from descriptive quality of life outcomes: mission impossible?

J V Chancellor1, D Coyle, M F Drummond.   

Abstract

Descriptive quality of life questionnaires are commonly administered in clinical trials, to evaluate outcomes from the patient's perspective alongside conventional clinical measures. When expressed in single index form as health state preference values (HSPVs), quality of life information is also relevant to economic evaluations. By combining HSPVs with survival information, quality adjusted life years (QALYs) may be derived for cost-utility analysis. Although HSPVs are rarely measured prospectively in cancer clinical trials, the UK Medical Research Council Cancer Therapy Committee recommends the routine administration of two specific quality of life questionnaires: the Rotterdam Symptom Checklist and the Hospital Anxiety Depression Scale. This study explores two potential methods for secondary derivation of HSPVs from these instruments, using data gathered in a clinical trial of two forms of radiotherapy for non-small cell cancer of the bronchus. The first method, secondary mapping to existing utility scales, was found to be infeasible from the above questionnaires. The second method used factor analysis to summarize the descriptive quality of life data collected through the questionnaires. This revealed five distinct factors prevalent in the trial population. Using these factors, simplified health state scenarios were developed from which direct measurement of HSPVs was feasible. As the resulting HSPVs and any QALYs that may be derived from them are cancer specific, their potential value in informing resource allocation would be limited to decisions within oncology services.

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Year:  1997        PMID: 9161116     DOI: 10.1023/a:1026494218030

Source DB:  PubMed          Journal:  Qual Life Res        ISSN: 0962-9343            Impact factor:   4.147


  17 in total

1.  Economic analysis alongside clinical trials. Revisiting the methodological issues.

Authors:  M F Drummond; L Davies
Journal:  Int J Technol Assess Health Care       Date:  1991       Impact factor: 2.188

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Authors:  A Williams
Journal:  Br Med J (Clin Res Ed)       Date:  1985-08-03

3.  Framing and labelling effects in health descriptions: quality adjusted life years for treatment of breast cancer.

Authors:  K Gerard; M Dobson; J Hall
Journal:  J Clin Epidemiol       Date:  1993-01       Impact factor: 6.437

4.  Quality of life of lung cancer patients in a randomized clinical trial evaluated by a psychosocial well-being questionnaire.

Authors:  S Kaasa; A Mastekaasa; S Naess
Journal:  Acta Oncol       Date:  1988       Impact factor: 4.089

Review 5.  Preliminary experience with quality of life evaluation in patients with lung cancer.

Authors:  P Hopwood; N Thatcher
Journal:  Oncology (Williston Park)       Date:  1990-05       Impact factor: 2.990

6.  Quality of life assessment during chemotherapy for non-small cell lung cancer.

Authors:  P K Maasilta; J K Rautonen; M T Mattson; K V Mattson
Journal:  Eur J Cancer       Date:  1990       Impact factor: 9.162

Review 7.  The quality of life of cancer patients: a review of the literature.

Authors:  J C de Haes; F C van Knippenberg
Journal:  Soc Sci Med       Date:  1985       Impact factor: 4.634

8.  Describing health states. Methodologic issues in obtaining values for health states.

Authors:  H Llewellyn-Thomas; H J Sutherland; R Tibshirani; A Ciampi; J E Till; N F Boyd
Journal:  Med Care       Date:  1984-06       Impact factor: 2.983

9.  Who should measure quality of life, the doctor or the patient?

Authors:  M L Slevin; H Plant; D Lynch; J Drinkwater; W M Gregory
Journal:  Br J Cancer       Date:  1988-01       Impact factor: 7.640

10.  Assessing quality of life in cancer patients.

Authors:  P Maguire; P Selby
Journal:  Br J Cancer       Date:  1989-09       Impact factor: 7.640

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  13 in total

1.  The effect of individually assessed preference weights on the relationship between holistic utilities and nonpreference-based assessment.

Authors:  S J Jansen; A M Stiggelbout; M A Nooij; J Kievit
Journal:  Qual Life Res       Date:  2000       Impact factor: 4.147

2.  Health-related quality of life research and the capability approach of Amartya Sen.

Authors:  M A Verkerk; J J Busschbach; E D Karssing
Journal:  Qual Life Res       Date:  2001       Impact factor: 4.147

3.  Using the effect size to model change in preference values from descriptive health status.

Authors:  Kristy Sanderson; Gavin Andrews; Justine Corry; Helen Lapsley
Journal:  Qual Life Res       Date:  2004-09       Impact factor: 4.147

Review 4.  Evaluation of content on EQ-5D as compared to disease-specific utility measures.

Authors:  Fang-Ju Lin; Louise Longworth; A Simon Pickard
Journal:  Qual Life Res       Date:  2012-06-23       Impact factor: 4.147

5.  Towards more consistent use of generic quality-of-life instruments.

Authors:  Mattias Neyt
Journal:  Pharmacoeconomics       Date:  2010       Impact factor: 4.981

6.  Validity and feasibility of the use of condition-specific outcome measures in economic evaluation.

Authors:  Elly A Stolk; Jan J V Busschbach
Journal:  Qual Life Res       Date:  2003-06       Impact factor: 4.147

7.  Korean guidelines for pharmacoeconomic evaluation (second and updated version) : consensus and compromise.

Authors:  Seungjin Bae; Soook Lee; Eun Young Bae; Sunmee Jang
Journal:  Pharmacoeconomics       Date:  2013-04       Impact factor: 4.981

8.  Estimating importance weights for the IWQOL-Lite using conjoint analysis.

Authors:  A Brett Hauber; Ateesha F Mohamed; F Reed Johnson; Olatoye Oyelowo; Bradley H Curtis; Cheryl Coon
Journal:  Qual Life Res       Date:  2010-03-04       Impact factor: 4.147

9.  Exploring the impact of changes in neurogenic urinary incontinence frequency and condition-specific quality of life on preference-based outcomes.

Authors:  William Hollingworth; Jonathan D Campbell; Jonathan Kowalski; Arliene Ravelo; Isabelle Girod; Andrew Briggs; Sean D Sullivan
Journal:  Qual Life Res       Date:  2010-01-22       Impact factor: 4.147

10.  The use of disease-specific outcome measures in cost-utility analysis: the development of Dutch societal preference weights for the FACT-L scale.

Authors:  Leida M Lamers; Carin A Uyl-de Groot; Ivonne Buijt
Journal:  Pharmacoeconomics       Date:  2007       Impact factor: 4.981

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