Using a patient-centered approach to benefit-harm assessment in treatment decision-making: a case study in uveitis

Abstract

Background Synthesizing evidence from comparative effectiveness trials can be difficult since multiple outcomes of different importance are to be considered. The goal of this study was to demonstrate an approach to conducting quantitative benefit-harm assessment that considers patient preferences. Methods We conducted a benefit-harm assessment using data from the Multicenter Uveitis Steroid Treatment Trial that compared corticosteroid implant versus systemic corticosteroids and immunosuppression in non-infectious intermediate, posterior, and panuveitis. We focused on clinical outcomes considered important to patients, including visual acuity, development of cataracts/glaucoma, need for eye surgery, prescription-requiring hypertension, hyperlipidemia and infections. Patient preferences elicited in a recent survey were then incorporated into our assessment of the benefit-harm balance. Results Benefit-harm metrics were calculated for each time point that summarized the numbers of outcomes, caused or prevented by implant therapy versus systemic therapy if 1000 patients were treated. The benefit-harm metric was -129 (95% CI: -242 to -14), -317 (-436 to -196), -390 (-514 to -264) and -526 (-687 to -368) at 6, 12, 18, and 24 months follow-up, respectively, suggesting that systemic therapy may have a better benefit-harm balance. However, measures of quality of life for patients treated with implant therapy were found to be better than patients treated with systemic therapy over the same time period. Conclusions Results of benefit-harm assessment were different from the prospectively collected quality of life data during trial follow-up. Future studies should explore the reasons for such discrepancies and the strength and weakness of each method to assess treatment benefits and harms.

Perceived Importance of the Benefits and Harms of Colorectal Cancer Screening: A Best-Worst Scaling Study

“…A limitation of this study is that there were missing values in the best-worst scaling questions. Even though the best-worst scaling method efficiently measures preferences and overcomes the methodological and psychometric weaknesses of other methods, 43,44 58% of respondents had at least one missing value in one of the ten choice sets. The increase in missing values with increasing number of choice sets indicates that respondents became bored or annoyed rather than experiencing difficulty with answering the questions.…”