Evaluating the Trade-offs Men with Localised Prostate Cancer Make Between the Risks and Benefits of Treatments: The COMPARE Study

PURPOSE: COMPARE (COMparing treatment options for ProstAte cancer) aimed to evaluate and quantify the trade-offs patients make between different aspects of active surveillance and definitive therapy. METHODS: A Discrete Choice Experiment (DCE) tool was used to elicit patients' preferences for different treatment characteristics in 34 urology departments. Patients with localised prostate cancer completed the DCE within one week of being diagnosed and before they made treatment decisions. The DCE was pre-tested (N=5) and piloted (n=106) with patients. Patients chose their preferred treatment profile based on six characteristics: treatment type (active surveillance, focal therapy, radical therapy), return to normal activities, erectile function, urinary function, not needing more cancer treatment and 10-15 year cancer-specific survival. Different tools were designed for low-intermediate (n=468) and high-risk (n=166) patients. An error-components conditional logit model was used to estimate preferences and trade-offs between treatment characteristics. RESULTS: Low-intermediate risk patients were willing to trade 6.99% absolute decrease in survival to have active surveillance over definitive therapy. They were willing to trade 0.75%, 0.46% and 0.19% absolute decrease in survival for a one-month reduction in time-to-return to normal activities, and 1% absolute improvements in urinary and sexual function, respectively. High-risk patients were willing to trade 3.10%, 1.04% and 0.41% absolute decrease in survival for a one-month reduction in time-to-return to normal activities and 1% absolute improvements in urinary and sexual function, respectively. CONCLUSIONS: Patients with low-intermediate risk prostate cancer preferred active surveillance to definitive therapy. Patients of all risks were willing to trade-off cancer-specific survival for improved quality-of-life.Registration:clinicaltrials.gov Registration Identifier NCT01177865Funding:Medical Research Council (UK) (grant reference: G1002509)

Stated preferences over job characteristics: a panel study

When making choices over jobs with different characteristics, what trade‐offs are decision‐makers willing to make? Such a question is difficult to address using typical household surveys that provide a limited amount of information on the attributes of the jobs. To address this question, a small but growing number of studies have turned to the use of stated preference experiments; but the extent to which stated choices by respondents reflect systematic trade‐offs across job characteristics remains an open question. We use two popular types of experiments (profile case best–worst scaling and multi‐profile case best–worst scaling) to elicit job preferences of nursing students and junior nurses in Australia. Each person participated in both types of experiments twice, within a span of about 15 months. Using a novel joint likelihood approach that links a decision‐maker's preferences across the two types of experiments and over time, we find that the decision‐makers make similar trade‐offs across job characteristics in both types of experiments and in both time periods, except for the trade‐off between salary and other attributes. The valuation of salary falls significantly over time relative to other job attributes for both types of experiments. Also, within each period, salary is less valued in the profile case compared to the more traditional multi‐profile case

Preferences of older adults for healthcare models designed to improve care coordination: Evidence from Western Switzerland.

Implementing innovations in care delivery in Switzerland is challenging due to the fragmented nature of the system and the specificities of the political process (i.e., direct democracy, decentralized decision-making). In this context, it is particularly important to account for population preferences when designing policies. We designed a discrete choice experiment to study population preferences for coordination-improving care models. Specifically, we assessed the relative importance of model characteristics (i.e., insurance premium, presence of care coordinator, access to specialists, use of EMR, cost-sharing for chronic patients, incentives for informal care), and predicted uptake under different policy scenarios. We accounted for heterogeneity in preferences for the status quo option using an error component logit model. Respondents attached the highest importance to the price attribute (i.e. insurance premium) (0.31, CI: 0.27- 0.36) and to the presence of a care coordinator (0.27, CI: 0.23 - 0.31). Policy scenarios showed for instance that gatekeeping would be preferred to free access to specialists if the model includes a GP or an interprofessional team as a care coordinator. Although attachment to the status quo is high in the studied population, there are potential ways to improve acceptance of alternative care models by implementation of positively valued innovations