Patient-Reported Outcomes/Preference-based Research

W4: COMPARING APPLES AND ORANGES: USING CONJOINT-ANALYSIS DATA TO OBTAIN SEVEN QUANTITATIVE BENEFIT-RISK MEASURES
Discussion Leaders: A. Brett Hauber PhD, Senior Economist and Global Head, Health Preference Assessment, RTI Health Solutions, Research Triangle Park, NC, USA; F Reed Johnson PhD, Senior Fellow and Principal Economist, Health Preference Assessment, RTI Health Solutions, Research Triangle Park, NC, USA; John FP Bridges PhD, Assistant Professor, Bloomberg School of Public Health, Health Policy and Management, Johns Hopkins University, Baltimore, MD, USA

PURPOSE: This interactive workshop will introduce participants to the conceptual and empirical basis for quantifying benefit-risk tradeoff preferences using conjoint-analysis.  The workshop will give participants new tools for assessing stakeholders’ risk tolerance for clinical benefits of new pharmaceuticals. 

DESCRIPTION: While much effort has been devoted to developing more accurate measures of efficacy and safety, decision-makers are left with the problem of comparing outcome measures expressed in different metrics.  Regulatory agencies such as EMEA and the FDA are actively exploring quantitative methods to inform benefit-risk decisions.  Efficacy-safety tradeoffs also are critical in drug-development decisions.  Choice-format conjoint-analysis surveys (also known as discrete-choice experiments) offer an empirical solution to estimating stakeholders’ willingness to accept such tradeoffs. 
Data on benefit-risk tradeoff preferences can provide metrics not previously available to decision makers to facilitate direct comparisons of efficacy and safety. Conjoint analysis is based on eliciting trade-off preferences in response to a set of experimental stimuli.  When a conjoint-analysis study obtains preference data on efficacy, treatment-related risks, and time durations, it is possible to derive seven benefit-risk metrics from the same dataset.   These metrics include: 1) Minimum acceptable benefit for a given level of risk; 2). Maximum acceptable risk for a given level of benefit; 3) Net effectiveness margin; 4) Net safety margin; 5) Maximum acceptable number needed to treat; 6) Minimum acceptable number needed to harm; 7) Healthy-years equivalents. 
The workshop will present the conceptual rationale these metrics and illustrate their estimation using data from a general-population study of Alzheimer’s-disease (AD) treatment preferences.  An exercise will enable workshop participants to assess their own benefit-risk tradeoff preferences for AD outcomes and compare their preferences with the general-population sample.  This workshop is designed for policy makers, clinicians, researchers, patient advocates, students, and industry experts interested in acquiring new tools to inform benefit-risk decisions.