Although the checklist should not be interpreted as endorsing any specific methodological approach to conjoint analysis, it can facilitate future training activities and discussions of good research practices for the application of conjoint-analysis methods in health care studies.

Stated-preference methods are a class of evaluation techniques for studying the preferences of patients and other stakeholders. While these methods span a variety of techniques, conjoint-analysis methods-and particularly discrete-choice experiments (DCEs)-have become the most frequently applied approach in health care in recent years. Experimental design is an important stage in the development of such methods, but establishing a consensus on standards is hampered by lack of understanding of available techniques and software. This report builds on the previous ISPOR Conjoint Analysis Task Force Report: Conjoint Analysis Applications in Health-A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. This report aims to assist researchers specifically in evaluating alternative approaches to experimental design, a difficult and important element of successful DCEs. While this report does not endorse any specific approach, it does provide a guide for choosing an approach that is appropriate for a particular study. In particular, it provides an overview of the role of experimental designs for the successful implementation of the DCE approach in health care studies, and it provides researchers with an introduction to constructing experimental designs on the basis of study objectives and the statistical model researchers have selected for the study. The report outlines the theoretical requirements for designs that identify choice-model preference parameters and summarizes and compares a number of available approaches for constructing experimental designs. The task-force leadership group met via bimonthly teleconferences and in person at ISPOR meetings in the United States and Europe. An international group of experimental-design experts was consulted during this process to discuss existing approaches for experimental design and to review the task force's draft reports. In addition, ISPOR members contributed to developing a consensus report by submitting written comments during the review process and oral comments during two forum presentations at the ISPOR 16th and 17th Annual International Meetings held in Baltimore (2011) and Washington, DC (2012).

Conjoint analysis is a stated-preference survey method that can be used to elicit responses that reveal preferences, priorities, and the relative importance of individual features associated with health care interventions or services. Conjoint analysis methods, particularly discrete choice experiments (DCEs), have been increasingly used to quantify preferences of patients, caregivers, physicians, and other stakeholders. Recent consensus-based guidance on good research practices, including two recent task force reports from the International Society for Pharmacoeconomics and Outcomes Research, has aided in improving the quality of conjoint analyses and DCEs in outcomes research. Nevertheless, uncertainty regarding good research practices for the statistical analysis of data from DCEs persists. There are multiple methods for analyzing DCE data. Understanding the characteristics and appropriate use of different analysis methods is critical to conducting a well-designed DCE study. This report will assist researchers in evaluating and selecting among alternative approaches to conducting statistical analysis of DCE data. We first present a simplistic DCE example and a simple method for using the resulting data. We then present a pedagogical example of a DCE and one of the most common approaches to analyzing data from such a question format-conditional logit. We then describe some common alternative methods for analyzing these data and the strengths and weaknesses of each alternative. We present the ESTIMATE checklist, which includes a list of questions to consider when justifying the choice of analysis method, describing the analysis, and interpreting the results.

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.