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05 June 2018

We are interested in the problem of decision-making for a portfolio of combination therapies. Several methods exist in the literature for portfolio planning which use stochastic programming techniques. However, the existing methods do not take into consideration the differences between single agent drug development and combination drug development. One of the key considerations to be made in a portfolio containing combinations is the potential to share information across studies of combinations that have at least one treatment in common. We consider a Bayesian framework where the success probability of a particular combination study is found using both historical data on the combination and information from related combination studies. This framework allows us to use all the available information regarding a combination, including emerging information, and feed this into the portfolio-level decisions that are made. We illustrate the developed methodology on sample portfolios and highlight the utility of incorporating related information.

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