included in the conjoint questions or by conducting split-sample analyses. Latent-class models allow the data to determine the optimal division of observations into groups with similar preferences (28).
ix. Results and conclusions
Those using conjoint analysis are often prone to making inferences and predictions that go beyond what the data and methods can support, and in outcomes research it is especially important to ask, “Were the results and conclusions valid?”
Evaluating the validity of results and conclusions requires consideration of the research question as well as other aspects of the design and analysis. The results should present the statistical findings in the context of the research question and should be presented in sufficient detail. The results should state which attributes/levels (and interaction terms, if relevant) included in the conjoint task were or were not significant and report uncertainty associated with estimates. Findings should be interpreted in the context of the choice being considered.
For example, in the multiple sclerosis example, the results could indicate that the rate of relapse was a significant attribute and a negative coefficient might imply higher rates of relapse were less preferred. If attributes/levels were found to be non-significant in the statistical analysis, these findings should also be clearly stated in the results. Results also should provide interpretation of the relative value of specific attributes, such as how the acceptable waiting time for non-emergency survey varies with the rate of surgical errors (i.e., the marginal willingness-to-wait for a reduced rate(s) of surgical errors). Statistical uncertainty should be reported in a manner consistent with the type of model selected. If alternative model specifications were tested, the results of these alternative analyses should be described if not presented in full.
Limitations of the study and the potential effect(s) of these limitations on results should be clearly identified. Limitations can arise from selection of attributes and/or levels, such as simplifications adopted during survey development in order to generate a feasible design, possible correlation among selected attributes, and other design features such as the inclusion or exclusion of an opt-out option. Assumptions underlying the analytic approach may also affect interpretation of results and should be discussed. If the study population is not representative of the population, this may limit generalizability of findings and any extrapolation of results beyond the study population should be qualified and discussed.
The conclusion section should identify key findings of the study in the context of the original research question. A key element of any research study is to provide a relevant framework for interpreting the results: whether the results are consistent with or differ from existing studies in the literature and how this study extends existing research should be clearly identified and discussed.
x. Study presentation
Finally, when evaluating a conjoint analysis study, the researcher should ask, “Was the study presented well and completely?”
The importance and context of the study must be adequately motivated so as to answer the “so what” question. First, the key background literature should be cited to place the study in an appropriate clinical or health policy
ISPOR Conjoint Analysis in Health Task Force Report