  In this paper, a methodology is proposed that enables to analyze the
sensitivity of the outcome of a therapy to unavoidable high dispersion of the
patient specific parameters on one hand and to the choice of the parameters
that define the drug delivery feedback strategy on the other hand. More
precisely, a method is given that enables to extract and rank the most influent
parameters that determine the probability of success/failure of a given
feedback therapy for a given set of initial conditions over a cloud of
realizations of uncertainties. Moreover predictors of the expectations of the
amounts of drugs being used can also be derived. This enables to design an
efficient stochastic optimization framework that guarantees safe contraction of
the tumor while minimizing a weighted sum of the quantities of the different
drugs being used. The framework is illustrated and validated using the example
of a mixed therapy of cancer involving three combined drugs namely: a
chemotherapy drug, an immunology vaccine and an immunotherapy drug. Finally, in
this specific case, it is shown that dash-boards can be built in the 2D-space
of the most influent state components that summarize the outcomes'
probabilities and the associated drug usage as iso-values curves in the reduced
state space.
