SAT0099 Preferences of Patients with Spondyloarthritis for the Items of the ASAS Health Index: A Best Worst Scaling: Table 1

Pros and cons of conjoint analysis of discrete choice experiments to define classification and response criteria in rheumatology

PURPOSE OF REVIEW

Conjoint analysis of choice or preference data has been used in marketing for over 40 years but has appeared in healthcare settings much more recently. It may be a useful technique for applications within the rheumatology field.

RECENT FINDINGS

Conjoint analysis in rheumatology contexts has mainly used the approaches implemented in 1000Minds Ltd, Dunedin, New Zealand, Sawtooth Software, Orem UT, USA. Examples include classification criteria, composite response criteria, service prioritization tools and utilities assessment. Limitations imposed by very many attributes can be managed using new techniques. Conjoint analysis studies of classification and response criteria suggest that the assumption of equal weighting of attributes cannot be met, which challenges traditional approaches to composite criteria construction. Weights elicited through choice experiments with experts can derive more accurate classification criteria, than unweighted criteria. Studies that find significant variation in attribute weights for composite response criteria for gout make construction of such criteria problematic.

SUMMARY

Better understanding of various multiattribute phenomena is likely to increase with increased use of conjoint analysis, especially when the attributes concern individual perceptions or opinions. In addition to classification criteria, some applications for conjoint analysis that are emerging in rheumatology include prioritization tools, remission criteria, and utilities for life areas.