Conjoint Analysis of Patient Preferences in Medical Management of Osteoarthritis of the Knee
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.
Allocation: Randomized, Endpoint Classification: Efficacy Study, Intervention Model: Parallel Assignment, Masking: Single Blind (Subject), Primary Purpose: Treatment
Change in osteoarthritis treatment (for instance, change from an NSAID to capsaicin cream) as measured by follow-up telephone interview
Simon Whitney, M.D.
Baylor College of Medicine
United States: Federal Government
7 U18 HS016093 Leveraged
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