Conjoint Analysis of Treatment Preferences for Osteoarthritis

Osteoarthritis (OA) is a major cause of disability in the elderly, second only to cardiovascular disease. The medical treatment of OA alleviates symptoms, but does not halt disease progression. Exercise is an effective intervention but for patients who do not get adequate relief from exercise and whose disease is not so severe as to warrant joint replacement, there are a variety of intermediate steps including medication and joint injection. There are nontrivial tradeoffs between these choices. This project explores the choices made by patients who have significant osteoarthritis of the knee using specialized computer software as a decision aid. Traditional decision aids present information in ways that help patients make decisions that are consistent with their values. However, this sort of decision aid usually provides no feedback for the clinician or researcher about the patient's thoughts, preferences, or reasoning. We propose to use conjoint analysis, an analytic tool for assessing preferences that has been used extensively in marketing but has only recently been introduced into medical decision making. In conjoint analysis, the consumer (in the marketing context) or subject (in the medical research context) is presented with pairs of choices. The marketing researcher might ask, for instance, if the consumer would rather have a $1000 laptop with 250 MB of RAM, or a $1200 laptop with 500 MB of RAM. The answer allows the accurate calculation of the subject's utilities for both money and RAM. Extending the questions to other elements allows utilities for the laptop's speed, weight, battery life, and screen size to be calculated and allows the computer maker to optimize its product lines. Instead of one sweet spot where price and features are at a happy medium, every laptop offered can be perceived by potential consumers as offering reasonable value for the money. Fraenkel and others have used conjoint analysis in the study of osteoarthritis and rheumatoid arthritis. Conjoint analysis presents choice pairs to subjects; for instance, how would you feel about a cream that offered an extremely low risk of complications with only moderate relief in symptoms, versus a medication that offered a moderate risk of major complications and better symptom relief? As a result of this process, utilities are generated mathematically for each of the preferences. Because we know relatively little about how patients feel about using conjoint analysis, and about making tradeoffs among the factors that conjoint analysis permits us to assess, this project will also utilize patient focus groups to explore these issues.