BSWG KOL Lecture Series, L14

Event Date:



Title: Benefit-Risk Assessment Using Bayesian Discrete Choice Experiment

Presenter: Saurabh Mukhopadhyay, PhD, AbbVie

Abstract: Early assessment of benefit-risk balance of a treatment is very important in a drug development program to understand the medicinal product’s utility in a population of interest. Benefit-risk assessment about treatments however are often complex and involve tradeoffs between multiple, sometimes conflicting, assessments of benefits and risks. Discrete choice experiments are used in health outcomes research to assess tradeoffs in preferences, but they often impose a high cognitive burden to assess multiple attributes and the requirement for a large pool of respondents. A novel Bayesian framework that borrows strength from respondents will be discussed. The framework allows to conduct the discrete choice experiments with only a limited number of respondents. Furthermore, this framework only requires respondents to choose from a few pairs of profiles to state their preferences, thus drastically reducing the cognitive burden. Specifically, a hierarchical Bayes benefit-risk (HBBR) model and associated discrete choice experiment will be discussed. In this framework each questionnaire consists of a few pairs of profiles that differ in only one benefit and one risk attribute level thereby making it very easy to state preferences. Also, by design, each respondent needs to evaluate only a fraction of all choice pairs; thus, respondents would not become fatigued from a long questionnaire. In addition to making the survey task operationally efficient as described above, easy-to-state-preference questionnaires, with a modest number of preference items per respondent, are also expected to produce very high-quality preference data. The proposed HBBR model can then be utilized to estimate the benefit and risk utilities of various level within each attribute. These ‘part-worth’ estimates can be finally combined to estimate overall benefit-risk scores. This new method is illustrated with a pilot experiment incorporating experts’ preferences in an oncology setting and fitting the HBBR model to the processed response data using an R-package developed for this purpose. Ultimately, patients are the most important voice in the benefit–risk balance. Therefore, using a simulated data and an augmented model it will be further shown how to incorporate patients’ characteristics to obtain a more precise estimate of benefit-risk preferences.

Reference: Mukhopadhyay, S., Dilley, K., Oladipo, A. and Jokinen, J., 2019. Hierarchical Bayesian Benefit–Risk Modeling and Assessment Using Choice Based Conjoint. Statistics in Biopharmaceutical Research, 11(1), pp.52-60
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Starting Time:

11:00 am

Ending Time:

1:00 pm