Literature DB >> 10874373

A role for conjoint analysis in technology assessment in health care?

M Ryan1.   

Abstract

The aim of this paper is to demonstrate the use of conjoint analysis (CA) in health services research. Conjoint analysis is first explained, with emphasis on the history of the technique, followed by an explanation of how to carry out such a study and how the results from such a study can be used. The technique is demonstrated with reference to a study that looks at the benefits of in vitro fertilization. It is shown how CA can be used to estimate the relative importance of attributes, the trade-offs individuals make between these attributes, willingness to pay if cost is included as an attribute, and utility or benefit scores for different ways of providing a service. The paper then considers the potential advantages of CA over other, more commonly used benefit assessment instruments. Finally, there is discussion of the issues raised in the design and analysis of CA studies. It is concluded that these issues must be addressed before the technique becomes an established instrument for technology assessment.

Mesh:

Year:  1999        PMID: 10874373

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


  31 in total

1.  Acceptability of willingness to pay techniques to consumers.

Authors:  Susan J Taylor; Carol L Armour
Journal:  Health Expect       Date:  2002-12       Impact factor: 3.377

2.  Can high quality overcome consumer resistance to restricted provider access? Evidence from a health plan choice experiment.

Authors:  Katherine M Harris
Journal:  Health Serv Res       Date:  2002-06       Impact factor: 3.402

3.  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

4.  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

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

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

6.  Conjoint analysis: a 'new' way to evaluate patients' preferences.

Authors:  Sarah T Hawley
Journal:  Patient       Date:  2008-12-01       Impact factor: 3.883

7.  Measuring Preferences for Colorectal Cancer Screening: What are the Implications for Moving Forward?

Authors:  Deborah Marshall; S Elizabeth McGregor; Gillian Currie
Journal:  Patient       Date:  2010-06-01       Impact factor: 3.883

8.  Factors that influence prescribers in their selection and use of COX-2 selective inhibitors as opposed to non-selective NSAIDs.

Authors:  Anna I Gunnarsdóttir; Moira Kinnear
Journal:  Pharm World Sci       Date:  2005-08

9.  Elicitation of ostomy pouch preferences: a discrete-choice experiment.

Authors:  Ole Bonnichsen
Journal:  Patient       Date:  2011       Impact factor: 3.883

10.  The Best of Both Worlds: An Example Mixed Methods Approach to Understand Men's Preferences for the Treatment of Lower Urinary Tract Symptoms.

Authors:  Divine Ikenwilo; Sebastian Heidenreich; Mandy Ryan; Colette Mankowski; Jameel Nazir; Verity Watson
Journal:  Patient       Date:  2018-02       Impact factor: 3.883

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.