The Patient: Patient-Centered Outcomes Research

, Volume 3, Issue 4, pp 249–256 | Cite as

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 MarshallEmail author
  • John F. P. Bridges
  • Brett Hauber
  • Ruthanne Cameron
  • Lauren Donnalley
  • Ken Fyie
  • F. Reed Johnson
Review Article


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.


Health Literature Latent Class Analysis Choice Task Conjoint Analysis Supplemental Digital Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Several authors (DAM, JFB, ABH, FRJ) are members of the ISPOR Conjoint Analysis in Health Task Force. A complete list of members is available on the ISPOR website. Deborah Marshall is supported by the Canada Research Chair program. This study was funded as part of an unrestricted grant from Pfizer through the John Hopkins Bloomberg School of Public Health and a Starter Grant to Deborah Marshall from the University of Calgary. Lauren Donnalley was supported as a reviewer by a research fellowship to Reed Johnson from Research Triangle Institute.

The authors would like to thank Joanna Dionne for her assistance in reviewing some of the articles identified in this search and Laura Banfield, an information specialist from the McMaster University Health Sciences Library, who devised the literature search strategy.

Supplementary material

40271_2012_3040249_MOESM1_ESM.pdf (193 kb)
Supplementary material, approximately 197 KB.


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Copyright information

© Adis Data Information BV 2010

Authors and Affiliations

  • Deborah Marshall
    • 1
    • 2
    Email author
  • John F. P. Bridges
    • 3
  • Brett Hauber
    • 4
  • Ruthanne Cameron
    • 2
  • Lauren Donnalley
    • 3
  • Ken Fyie
    • 1
  • F. Reed Johnson
    • 3
  1. 1.Department of Community Health SciencesUniversity of CalgaryCalgaryCanada
  2. 2.Centre for Evaluation of MedicinesMcMaster UniversityHamiltonCanada
  3. 3.Health Policy and ManagementJohns Hopkins University, Bloomberg School of Public HealthBaltimoreUSA
  4. 4.Health Preference AssessmentRTI Health SolutionsResearch Triangle ParkUSA

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