An evaluation of patient preferences for osteoporosis medication attributes: results from the PREFER-US study.

OBJECTIVE To evaluate preferences for eight medication attributes that women may consider when evaluating prescription osteoporosis medications. RESEARCH DESIGN AND METHODS The eligible sample consisted of women aged 50 years or older who responded to the 2003 or 2004 Internet-based National Health and Wellness Survey as being diagnosed with osteoporosis, considering themselves at risk, or having a family history of osteoporosis. In this Internet survey (the PREFER survey), respondents were asked to: (1) force-rank order the eight attributes from one to eight according to their preferences and (2) separately rate the importance of each attribute on a Likert-type scale from 1 (extremely unimportant) to 7 (extremely important). RESULTS We collected 999 responses across 3 days from a sample of 3368 women and stopped compiling responses after achieving sample size targets. Drug effectiveness (e.g., ability to reduce the risk of fractures) was force ranked as the No. 1 preferred osteoporosis medication attribute by 37% of the sample. Side effects were force ranked as the No. 1 preferred medication attribute by 36% of the sample. Dosing frequency, dosing procedure, and formulation (i.e., how the drug is taken) were each force ranked as No. 1 by 2% or less of the sample. Drug effectiveness had the highest percentage of 'extremely important' responses (59%) followed by drug interactions (53%). Drug effectiveness was the highest-rated attribute (mean [S.D.] = 6.1 [1.6], median = 7), while dosing frequency was the lowest-rated attribute (mean [S.D.] = 4.7 [1.8], median = 5). CONCLUSIONS In our sample of women with a diagnosis of osteoporosis or at risk for osteoporosis, drug effectiveness was the most highly ranked and rated of eight osteoporosis medication attributes. Side effects and drug interactions were also highly ranked and rated. Healthcare providers should consider incorporating patient preferences for key medication attributes into their therapeutic decision-making processes.