BACKGROUND: Incorporation of patient preferences into treatment decisions is an essential component of medical care. Conjoint analysis is an established method of eliciting consumer preferences in market research and is being increasingly used to study patient preferences for health care. OBJECTIVE: To examine the value of Adaptive Conjoint Analysis (ACA), a unique method of performing conjoint analysis, and to evaluate patient treatment preferences. RESEARCH DESIGN: Interactive computer survey. SUBJECTS: Consecutive women (n = 103) with lupus followed in three community rheumatology practices. MEASURES: ACA was used to assess patients' relative preferences for specific cytotoxic medication characteristics, and to estimate the percentage of women preferring cyclophosphamide over azathioprine for different risk-benefit scenarios. RESULTS: All participants were able to complete the conjoint task in 14 +/-5 minutes. Of the nine medication characteristics studied, efficacy and risk for infection had the greatest impact on preference (each accounting for 20% of the variation in preferences), suggesting that patients consider differences in the risk for infection equally as important as differences in the probability of renal survival. Premenopausal women wanting more children were less likely to choose cyclophosphamide compared with their counterparts (56% vs. 80%, P = 0.04). Modest changes in the probability of renal survival or risk for major toxicity lowered the percentage of women preferring cyclophosphamide by more than 20%, irrespective of their desire for more children. CONCLUSIONS: ACA is a feasible method of assessing how patients consider specific medication characteristics and predicting treatment preferences under different risk-benefit scenarios. ACA may be a valuable tool to incorporate patient preferences into medical decision-making.
BACKGROUND: Incorporation of patient preferences into treatment decisions is an essential component of medical care. Conjoint analysis is an established method of eliciting consumer preferences in market research and is being increasingly used to study patient preferences for health care. OBJECTIVE: To examine the value of Adaptive Conjoint Analysis (ACA), a unique method of performing conjoint analysis, and to evaluate patient treatment preferences. RESEARCH DESIGN: Interactive computer survey. SUBJECTS: Consecutive women (n = 103) with lupus followed in three community rheumatology practices. MEASURES: ACA was used to assess patients' relative preferences for specific cytotoxic medication characteristics, and to estimate the percentage of women preferring cyclophosphamide over azathioprine for different risk-benefit scenarios. RESULTS: All participants were able to complete the conjoint task in 14 +/-5 minutes. Of the nine medication characteristics studied, efficacy and risk for infection had the greatest impact on preference (each accounting for 20% of the variation in preferences), suggesting that patients consider differences in the risk for infection equally as important as differences in the probability of renal survival. Premenopausal women wanting more children were less likely to choose cyclophosphamide compared with their counterparts (56% vs. 80%, P = 0.04). Modest changes in the probability of renal survival or risk for major toxicity lowered the percentage of women preferring cyclophosphamide by more than 20%, irrespective of their desire for more children. CONCLUSIONS: ACA is a feasible method of assessing how patients consider specific medication characteristics and predicting treatment preferences under different risk-benefit scenarios. ACA may be a valuable tool to incorporate patient preferences into medical decision-making.
Authors: Liana Fraenkel; Paul Falzer; Terri Fried; Minna Kohler; Ellen Peters; Robert Kerns; Howard Leventhal Journal: J Gen Intern Med Date: 2011-11-12 Impact factor: 5.128
Authors: Miguel A Ruiz; Felipe Heras; Agusti Alomar; Luis Conde-Salazar; Jesús de la Cuadra; Esther Serra; Francisco Regalado; Ralf Halbach Journal: Health Qual Life Outcomes Date: 2010-11-05 Impact factor: 3.186
Authors: Jessica Fishman; Peter O'Dwyer; Hien L Lu; Hope R Henderson; Hope Henderson; David A Asch; David J Casarett Journal: Cancer Date: 2009-02-01 Impact factor: 6.860