NextBestDualEndpoint-class: The class with the input for finding the next dose based on...
In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

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This rule first excludes all doses that exceed the probability maxOverdoseProb of having an overdose toxicity, as specified by the overdose interval overdose. Then, it picks under the remaining admissible doses the one that maximizes the probability to be in the target biomarker range, by default relative to the maximum biomarker level across the dose grid or relative to the Emax parameter in case a parametric model was selected (e.g. DualEndpointBeta, DualEndpointEmax)) However, is scale is set to "absolute" then the natural absolute biomarker scale can be used to set a target.

target: the biomarker target range, that needs to be reached. For example, (0.8, 1.0) and scale="relative" means we target a dose with at least 80% of maximum biomarker level. As an other example, (0.5, 0.8) would mean that we target a dose between 50% and 80% of the maximum biomarker level.
scale: either relative (default, then the target is interpreted relative to the maximum, so must be a probability range) or absolute (then the target is interpreted as absolute biomarker range)
overdose: the overdose toxicity interval (lower limit excluded, upper limit included)
maxOverdoseProb: maximum overdose probability that is allowed
targetThresh: which target probability threshold needs to be fulfilled before the target probability will be used for deriving the next best dose (default: 0.01)

# Target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)

myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

## now do it with an absolute target on the natural biomarker scale:

myNextBest <- NextBestDualEndpoint(target=c(200, 300),
                                   scale="absolute",
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)