Conjoint Analysis of Treatment Preferences for Osteoarthritis

Conjoint Analysis of Patient Preferences in Medical Management of Osteoarthritis of the Knee

The purpose of this study is to develop a conjoint analysis-based questionnaire and decision aid for patients with osteoarthritis of the knee and to compare the responses of two groups of subjects, one receiving only printed information about knee osteoarthritis, the other participating in a computer-based adaptive conjoint analysis program.

Study Overview

• Status: Completed
• Conditions: Osteoarthritis
• Intervention / Treatment: Behavioral: Standard of care for osteoarthritis treatment; Behavioral: Conjoint Analysis for Osteoarthritis

Detailed Description

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.

• Study Type: Observational
• Enrollment (Actual): 182

Contacts and Locations

Study Locations

• United States
  • Texas
    • Houston, Texas, United States, 77098
      • Baylor College of Medicine Family Medicine

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 65 years to 95 years (Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

People aged 65-95 with knee pain

Description

Inclusion Criteria:

• Age 65 or older
• Knee pain over the past month on most days
• Able to travel to Family Medicine offices, if in the treatment group
• Able to read and understand English
• Able to answer questions on a computer screen

Exclusion Criteria:

• Bleeding or non-bleeding ulcer within the last year
• History of ruptured ulcer (ever)
• History of GI bleeding (ever)
• Currently taking Coumadin or blood-thinning medication
• Diagnosis of lupus (ever), psoriatic arthritis (ever), gout (current or within past year), rheumatoid arthritis (ever), or coronary artery disease (ever)
• Prior total knee replacement or scheduled to get knee replacement in painful knee(s)
• Satisfied with current knee pain treatment
• Unable to get to a doctor for knee pain if needed

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Usual Care; Patients randomized to the control group will be sent the post-test measures suitably modified to reflect the fact that they did not participate in the conjoint analysis program. Four weeks after the post-test measures are completed, a staff member will call the subject to complete a 10 minute follow-up questionnaire to assess if any changes in treatment have occurred and to take further measurements (same measurements given to treatment group).	Behavioral: Standard of care for osteoarthritis treatment; Standard of care educational materials to inform patients about choices for knee pain.; Other Names: Osteoarthritis usual care
Conjoint Analysis Group; Patients randomized to the experimental group will meet the research staff to complete the conjoint analysis software and post-test measures. The post-test measures include preparedness for decision-making, personal uncertainty, osteoarthritis knowledge, arthritis self-efficacy, and satisfaction with the results of the conjoint analysis program. The in-person visit takes approximately 60 minutes to complete. Four weeks after the in-person visit, a staff member will call the subject to complete a 10 minute follow-up questionnaire to assess if any changes in treatment have occurred and to take further measurements (i.e. global pain assessment, arthritis self-efficacy, personal uncertainty, and osteoarthritis knowledge).	Behavioral: Conjoint Analysis for Osteoarthritis; Conjoint Analysis computer software to inform patients about choices for knee pain.; Other Names: Computer-assisted decision aid

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Change in osteoarthritis treatment (for instance, change from an NSAID to capsaicin cream) as measured by follow-up telephone interview	4 weeks

Secondary Outcome Measures

Outcome Measure	Time Frame
Ease of use, understandability, and suggestions for improvement of the computer decision aid	same day

Collaborators and Investigators

• Sponsor: Baylor College of Medicine
• Collaborators: M.D. Anderson Cancer Center; Agency for Healthcare Research and Quality (AHRQ)
• Investigators: Principal Investigator: Simon Whitney, M.D., Baylor College of Medicine

Study record dates

Study Major Dates

• Study Start: August 1, 2007
• Primary Completion (Actual): August 1, 2010
• Study Completion (Actual): December 1, 2010

Study Registration Dates

• First Submitted: October 28, 2009
• First Submitted That Met QC Criteria: October 28, 2009
• First Posted (Estimate): October 29, 2009

Study Record Updates

• Last Update Posted (Estimate): August 24, 2015
• Last Update Submitted That Met QC Criteria: August 20, 2015
• Last Verified: August 1, 2015

More Information

Terms related to this study

• Keywords: Knee pain, osteoarthritis, non-surgical treatment, age 65+
• Additional Relevant MeSH Terms: Joint Diseases; Musculoskeletal Diseases; Rheumatic Diseases; Arthritis; Osteoarthritis
• Other Study ID Numbers: 7 U18 HS016093 Leveraged; RFA HS 05-014