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You are here: Home / Innovation / Understanding heterogeneity for patient preference data and how it effects the benefit-risk ratio for treatments

Understanding heterogeneity for patient preference data and how it effects the benefit-risk ratio for treatments

By Alexander on 2019-05-28 0

Understanding heterogeneity for patient preference data and how it effects the benefit-risk ratio for treatments
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Interview with Marco Boeri

As statisticians in the medical field, we’re used to study subgroups of patients with respect to all kinds of biological variables: from demographics to genomics. This provides us with a good understanding of how the benefit-risk profile for a given patient looks like.

However, the patient might have a completely different view on the importance of the different benefits and risks. And importantly, these preferences might be less driven by biologic factors and more by personal experiences and situations as well as psychological traits. How can we assess patient preferences in this regard?

Marco Boeri and I worked on such questions in the past and some work has been presented at last years PSI conference. In todays episode, we give you some insights into what’s possible and how you can approach this problem.

We’ll dive into questions like:

  • Why’s exploring heterogeneity for preference data different, than for the usual endpoints in clinical studies?
  • What kind of factors, do we need to consider for patient preferences?
  • Is it also possible to cluster patients based on similar preferences?
  • What are potential implications in terms of treatment decisions or the benefit-risk ratio in different subgroups?

About Marco Boeri

Marco Boeri, PhD, is a Senior Research Economist at RTI-HS. Dr. Boeri was previously lecturer in Environmental Economics and has 10 years of experience in preference assessment in environmental and health economics and 2 years of experience in private financial sector in Marketing. Dr. Boeri has extensive knowledge and experience in experimental design, survey development and modelling data from discrete choice studies in health, food and environmental economics. His research focusses on comparing different and innovating preference analysis methods (i.e., regret minimization vs. utility maximization or structural choice modelling) at individual and household level. He has co-authored the first applications of the Random Regret Minimization model in both environmental and health economics and he has published in several applied economics journals across different disciplines including Pharmacoeconomics, Journal of Health Economics, Social Science and Medicine, Value in Health, Medical Decision Making, Preventive Medicine, Environmental and Resource Economics, Energy Economics, Transportation Research Part A, and the Journal of Economic Behavior and Organization, demonstrating the applicability of his methodological tool at top levels in different topics and fields.

Dr. Marco Boeri is interested in environmental and resource economics, health economics, energy economics, micro-econometrics, non-market valuation, choice experiments, preference analysis: regret minimization versus utility maximization, and consumer behavior.

References

FDA CDRH Obesity Device Preference Study: Ho, M. P., et al. (2015). “Incorporating patient-preference evidence into regulatory decision making.” Surg Endosc. (Free pdf version of paper!)

Further information from the FDA about Patient Preference Information (PPI) in Medical Device Decision-Making

IMI-Prefer

MDIC Patient Centered Benefit-Risk Framework Report Public Release, May 13,2015 .

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