A-6 INTEGRATING PATIENT PREFERENCES AND CLINICAL TRIAL DATA IN A BAYESIAN MODEL FOR QUANTITATIVE RISK-BENEFIT ASSESSMENT

Thursday, October 18, 2012: 2:45 PM
Regency Ballroom A/B (Hyatt Regency)
Health Services, and Policy Research (HSP)
Candidate for the Lee B. Lusted Student Prize Competition

Henk Broekhuizen, MSc.1, Karin G.M. Groothuis-Oudshoorn, PhD1, A. Brett Hauber, PhD2 and Maarten J. IJzerman, PhD1, (1)University of Twente, Enschede, Netherlands, (2)RTI Health Solutions, Research Triangle Park, NC

Purpose: Regulatory agencies show a growing interest in quantitative models for risk-benefit assessments to increase decision transparency. In addition, regulators increasingly incorporate the view of patients regarding benefit-risk trade offs. Although patient perspectives are sometimes taken into account through patient panels, little is known on how to integrate elicited preferences into the decision making process. There is also little knowledge on how to integrate these preferences with clinical performance data and how to use knowledge about the uncertainty surrounding both types of parameters (preference and performance). The objective of this study was to demonstrate how patient preferences can be integrated in a Bayesian framework for quantitative benefit-risk assessment. 

Method: An MCDA model was developed that integrates clinical trial data, patient preference information and the uncertainty surrounding these estimates. Stochastic characteristics of preference weights and drug performance parameters can be approximated from stated preference studies (e.g. conjoint analysis or direct rankings obtained from MCDA studies) and clinical performance data estimated from systematic reviews or RCT’s. Risk and benefit scores of drugs are then simulated using approximated distributions. All simulations of a particular drug where the weighted benefits are higher than the weighted risks are considered acceptable. Then, the acceptability is calculated. Using value of information metrics, residual uncertainty and the impact of reducing uncertainty on parameters are calculated. A ‘risk-benefit factsheet’ with acceptability graphs is provided, to facilitate decision makers in their appraisal.

Result: We applied the method in two cases, namely a case with anti-depressants and a case on colorectal cancer screening. For both cases we demonstrate the potential utility of applying the MCDA framework to the decision-making process.

Conclusion: Using Bayesian statistics it is possible to include patient preference in a quantitative risk-benefit assessment model. The model allows integration of stochastic uncertainty as well as (preference) heterogeneity. The study also demonstrates that comprehensive presentation of the data is possible. The usefulness of the approach needs to be determined in real-life case studies.