  Phase 1-2 designs provide a methodological advance over phase 1 designs for
dose finding by using both clinical response and toxicity. A phase 1-2 trial
still may fail to select a truly optimal dose. because early response is not a
perfect surrogate for long term therapeutic success. To address this problem, a
generalized phase 1-2 design first uses a phase 1-2 design's components to
identify a set of candidate doses, adaptively randomizes patients among the
candidates, and after longer follow up selects a dose to maximize long-term
success rate. In this paper, we extend this paradigm by proposing a design that
exploits an early treatment-related, real-valued biological outcome, such as
pharmacodynamic activity or an immunological effect, that may act as a mediator
between dose and clinical outcomes, including tumor response, toxicity, and
survival time. We assume multivariate dose-outcome models that include effects
appearing in causal pathways from dose to the clinical outcomes. Bayesian model
selection is used to identify and eliminate biologically inactive doses. At the
end of the trial, a therapeutically optimal dose is chosen from the set of
doses that are acceptably safe, clinically effective, and biologically active
to maximize restricted mean survival time. Results of a simulation study show
that the proposed design may provide substantial improvements over designs that
ignore the biological variable.
