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However, conjoint analysis may not be the only method for answering these research questions. In the first example, contingent valuation is a likely alternative for estimating patients’ willingness to pay to reduce the rate of relapse in multiple sclerosis. In the second example, observational data from a pilot program for reducing surgical errors in which surgical waiting times are increased may provide enough information to answer the research question. Therefore, researchers should identify not only whether conjoint analysis can be used to answer the research question, but also why conjoint analysis is preferable to alternative methods.

ii. Attributes and levels

A central feature of a conjoint analysis is the combination of the attributes and levels and is addressed in the second key point the in the checklist: “Were the attributes and attribute levels supported by evidence?”

The objective of conjoint analysis is to elicit preferences over the full range of attributes and attribute levels that define the profiles in the conjoint tasks. All attributes that potentially characterize the alternatives should be identified. In addition, the attribute levels should encompass the range that may be salient to subjects even if those levels are hypothetical or not feasible given current technology.

The identification of attributes should be supported by evidence regarding the potential range of preferences that people may have. Sources of evidence should include: literature reviews and other evidence regarding the impact of a disease and the nature of a health technology; clinical experts, and, perhaps most usefully, interviews or focus groups with individuals who represent the population from which study subjects likely will be drawn. The choice of whether focus groups or interviews should be used depends on many factors including the nature of the questions being asked and the types of people being included in the research. Simple thematic analysis is probably sufficient for guiding attribute selection. Such qualitative research will provide the basis for identifying the full set of attributes and possible levels that characterize the profiles in the preference space. Discussion with experts and further pilot testing with subjects can be used to narrow down the list of attributes if necessary.

The subset of attributes from the preference space that should be included in the conjoint tasks can be determined based on three criteria: relevance to the research question; relevance to the decision context; and whether attributes are potentially correlated. Attributes central to the research question or to the decision context must either be included in the conjoint tasks or held constant across all profiles in the conjoint tasks. For example, when eliciting patients’ preferences for a surgical intervention, the efficacy of the intervention almost certainly is an important outcome.

However, if the research is designed to estimate patients’ willingness to pay or willingness to wait to have a less- invasive surgical procedure, then it may make sense to exclude efficacy from the set of attributes and to inform participants that efficacy does not vary between the profiles. Similarly, it also is important to control for any potential attributes that are omitted from the conjoint tasks, but which correlate with attributes that are included in the conjoint tasks. In the US healthcare market, insurance coverage and out-of-pocket medical expenses for procedures are routine for many patients. Cost may be perceived as correlated with improvements in medical outcomes or access to advanced interventions. If cost is not included in such a study, it needs to be controlled for by holding it constant across profiles.

ISPOR Conjoint Analysis in Health Task Force Report

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