Literature DB >> 10507193

Patients' preferences regarding the process and outcomes of life-saving technology. An application of conjoint analysis to liver transplantation.

J Ratcliffe1, M Buxton.   

Abstract

The economic technique of conjoint analysis was used to assess the relative importance of health outcome versus several process attributes (e.g., waiting time, continuity of contact with the same medical staff) in determining patients' preferences for liver transplantation services. The attributes were established by reference to the literature and through initial qualitative interviews with liver transplant recipients (n = 12). Following a pilot study of 40 patients, a sample of patients (n = 213) who have received a liver transplant at the Queen Elizabeth Hospital in Birmingham were surveyed. The technique of conjoint analysis was used to ascertain the relative importance of the attributes included in the exercise and to estimate the marginal rates of substitution (MRS) between different attributes. A useable response rate of 89% was achieved. Although a small proportion of respondents (15%) exhibited dominant preferences for the chance of success attribute, the majority of respondents indicated that they would be prepared to exchange a reduction in health outcome for an improvement in the process characteristics of the liver transplantation service. The results of this study have potentially important implications for the assessment of the benefits of medical technologies since they suggest that, even in the extreme case of life-saving interventions, the preferences of respondents may not be dependent solely upon health outcomes but may also be determined by attributes associated with the process of care.

Entities:  

Keywords:  Empirical Approach; Health Care and Public Health

Mesh:

Year:  1999        PMID: 10507193

Source DB:  PubMed          Journal:  Int J Technol Assess Health Care        ISSN: 0266-4623            Impact factor:   2.188


  24 in total

1.  Using conjoint analysis to elicit preferences for health care.

Authors:  M Ryan; S Farrar
Journal:  BMJ       Date:  2000-06-03

2.  Measuring preferences for health care interventions using conjoint analysis: an application to HIV testing.

Authors:  Kathryn A Phillips; Tara Maddala; F Reed Johnson
Journal:  Health Serv Res       Date:  2002-12       Impact factor: 3.402

3.  Measuring what people value: a comparison of "attitude" and "preference" surveys.

Authors:  Kathryn A Phillips; F Reed Johnson; Tara Maddala
Journal:  Health Serv Res       Date:  2002-12       Impact factor: 3.402

4.  Discrete choice experiments in health economics. For better or for worse?

Authors:  Stirling Bryan; Paul Dolan
Journal:  Eur J Health Econ       Date:  2004-10

5.  The importance of drug adverse effects compared with seizure control for people with epilepsy: a discrete choice experiment.

Authors:  Andrew Lloyd; Emma McIntosh; Martin Price
Journal:  Pharmacoeconomics       Date:  2005       Impact factor: 4.981

6.  Assessing patients' preferences for characteristics associated with homeopathic and conventional treatment of asthma: a conjoint analysis study.

Authors:  J Ratcliffe; R Van Haselen; M Buxton; K Hardy; J Colehan; M Partridge
Journal:  Thorax       Date:  2002-06       Impact factor: 9.139

7.  Patient preferences and National Health Service costs: a cost-consequences analysis of cancer genetic services.

Authors:  Gethin L Griffith; Rhiannon Tudor Edwards; J Mark G Williams; Jonathon Gray; Val Morrison; Clare Wilkinson; Jim Turner; Barbara France; Paul Bennett
Journal:  Fam Cancer       Date:  2008-09-27       Impact factor: 2.375

Review 8.  The role of patient preferences in cost-effectiveness analysis: a conflict of values?

Authors:  John E Brazier; Simon Dixon; Julie Ratcliffe
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

9.  Consumer preference for dinoprostone vaginal gel using stated preference discrete choice modelling.

Authors:  Susan Taylor; Carol Armour
Journal:  Pharmacoeconomics       Date:  2003       Impact factor: 4.981

10.  Understanding preferences for disease-modifying drugs in osteoarthritis.

Authors:  Liana Fraenkel; Lisa Suter; Charles E Cunningham; Gillian Hawker
Journal:  Arthritis Care Res (Hoboken)       Date:  2014-08       Impact factor: 4.794

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