Literature DB >> 22273432

Conjoint Analysis Applications in Health - How are Studies being Designed and Reported?: An Update on Current Practice in the Published Literature between 2005 and 2008.

Deborah Marshall1, John F P Bridges, Brett Hauber, Ruthanne Cameron, Lauren Donnalley, Ken Fyie, F Reed Johnson.   

Abstract

Despite the increased popularity of conjoint analysis in health outcomes research, little is known about what specific methods are being used for the design and reporting of these studies. This variation in method type and reporting quality sometimes makes it difficult to assess substantive findings. This review identifies and describes recent applications of conjoint analysis based on a systematic review of conjoint analysis in the health literature. We focus on significant unanswered questions for which there is neither compelling empirical evidence nor agreement among researchers.We searched multiple electronic databases to identify English-language articles of conjoint analysis applications in human health studies published since 2005 through to July 2008. Two independent reviewers completed the detailed data extraction, including descriptive information, methodological details on survey type, experimental design, survey format, attributes and levels, sample size, number of conjoint scenarios per respondent, and analysis methods. Review articles and methods studies were excluded. The detailed extraction form was piloted to identify key elements to be included in the database using a standardized taxonomy.We identified 79 conjoint analysis articles that met the inclusion criteria. The number of applied studies increased substantially over time in a broad range of clinical applications, cancer being the most frequent. Most used a discrete-choice survey format (71%), with the number of attributes ranging from 3 to 16. Most surveys included 6 attributes, and 73% presented 7-15 scenarios to each respondent. Sample size varied substantially (minimum = 13, maximum = 1258), with most studies (38%) including between 100 and 300 respondents. Cost was included as an attribute to estimate willingness to pay in approximately 40% of the articles across all years.Conjoint analysis in health has expanded to include a broad range of applications and methodological approaches. Although we found substantial variation in methods, terminology, and presentation of findings, our observations on sample size, the number of attributes, and number of scenarios presented to respondents should be helpful in guiding researchers when planning a new conjoint analysis study in health.

Entities:  

Year:  2010        PMID: 22273432     DOI: 10.2165/11539650-000000000-00000

Source DB:  PubMed          Journal:  Patient        ISSN: 1178-1653            Impact factor:   3.883


  7 in total

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

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

Review 2.  Stated preference methods in health care evaluation: an emerging methodological paradigm in health economics.

Authors:  John F P Bridges
Journal:  Appl Health Econ Health Policy       Date:  2003       Impact factor: 2.561

Review 3.  Using discrete choice experiments to value health care programmes: current practice and future research reflections.

Authors:  Mandy Ryan; Karen Gerard
Journal:  Appl Health Econ Health Policy       Date:  2003       Impact factor: 2.561

4.  Things are Looking up Since We Started Listening to Patients: Trends in the Application of Conjoint Analysis in Health 1982-2007.

Authors:  John F P Bridges; Elizabeth T Kinter; Lillian Kidane; Rebekah R Heinzen; Colleen McCormick
Journal:  Patient       Date:  2008-12-01       Impact factor: 3.883

5.  Patient-based health technology assessment: a vision of the future.

Authors:  John F P Bridges; Christopher Jones
Journal:  Int J Technol Assess Health Care       Date:  2007       Impact factor: 2.188

6.  Conducting discrete choice experiments to inform healthcare decision making: a user's guide.

Authors:  Emily Lancsar; Jordan Louviere
Journal:  Pharmacoeconomics       Date:  2008       Impact factor: 4.981

7.  Maximising responses to discrete choice experiments: a randomised trial.

Authors:  Joanna Coast; Terry N Flynn; Chris Salisbury; Jordan Louviere; Tim J Peters
Journal:  Appl Health Econ Health Policy       Date:  2006       Impact factor: 2.561

  7 in total
  117 in total

1.  Patient preferences for first-line oral treatment for mild-to-moderate ulcerative colitis: a discrete-choice experiment.

Authors:  Paul Hodgkins; Paul Swinburn; Dory Solomon; Linnette Yen; Sarah Dewilde; Andrew Lloyd
Journal:  Patient       Date:  2012       Impact factor: 3.883

2.  Why not real economics?

Authors:  F Reed Johnson
Journal:  Pharmacoeconomics       Date:  2012-02-01       Impact factor: 4.981

3.  Health utility elicitation: is there still a role for direct methods?

Authors:  Lisa A Prosser; Scott D Grosse; Eve Wittenberg
Journal:  Pharmacoeconomics       Date:  2012-02-01       Impact factor: 4.981

4.  Consumer preferences for hearing aid attributes: a comparison of rating and conjoint analysis methods.

Authors:  John F P Bridges; Angela T Lataille; Christine Buttorff; Sharon White; John K Niparko
Journal:  Trends Amplif       Date:  2012-04-17

5.  Impact of educational and patient decision aids on decisional conflict associated with total knee arthroplasty.

Authors:  Sofia de Achaval; Liana Fraenkel; Robert J Volk; Vanessa Cox; Maria E Suarez-Almazor
Journal:  Arthritis Care Res (Hoboken)       Date:  2012-02       Impact factor: 4.794

6.  Physician preferences for bone metastasis drug therapy in Canada.

Authors:  J Arellano; J M González; Y Qian; M Habib; A F Mohamed; F Gatta; A B Hauber; J Posner; N Califaretti; E Chow
Journal:  Curr Oncol       Date:  2015-10       Impact factor: 3.677

7.  A Systematic Review of Discrete-Choice Experiments and Conjoint Analysis Studies in People with Multiple Sclerosis.

Authors:  Edward J D Webb; David Meads; Ieva Eskyte; Natalie King; Naila Dracup; Jeremy Chataway; Helen L Ford; Joachim Marti; Sue H Pavitt; Klaus Schmierer; Ana Manzano
Journal:  Patient       Date:  2018-08       Impact factor: 3.883

8.  Preferences and Stated Adherence for Antibiotic Treatment of Cystic Fibrosis Pseudomonas Infections.

Authors:  Ateesha Farah Mohamed; F Reed Johnson; Maria-Magdalena Balp; Frederico Calado
Journal:  Patient       Date:  2016-02       Impact factor: 3.883

9.  First-degree relatives of axial spondyloarthritis patients of the pre-SpA cohort would consider using medication in a preventive setting.

Authors:  Janneke J de Winter; Henriëtte M de Jong; Pythia T Nieuwkerk; Irene E van der Horst-Bruinsma; Dominique L Baeten; Marleen G van de Sande
Journal:  Clin Rheumatol       Date:  2018-10-23       Impact factor: 2.980

10.  Towards personalizing treatment for depression : developing treatment values markers.

Authors:  Marsha N Wittink; Knashawn H Morales; Mark Cary; Joseph J Gallo; Stephen J Bartels
Journal:  Patient       Date:  2013       Impact factor: 3.883

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