  Determining the extent to which a patient is benefiting from cancer therapy
is challenging. Criteria for quantifying the extent of "tumor response"
observed within a few cycles of treatment have been established for various
types of solid as well as hematologic malignancies. These measures comprise the
primary endpoints of phase II trials. Regulatory approvals of new cancer
therapies, however, are usually contingent upon the demonstration of superior
overall survival with randomized evidence acquired with a phase III trial
comparing the novel therapy to an appropriate standard of care treatment. With
nearly two thirds of phase III oncology trials failing to achieve statistically
significant results, researchers continue to refine and propose new surrogate
endpoints. This article presents a Bayesian framework for studying
relationships among treatment, patient subgroups, tumor response and survival.
Combining classical components of mediation analysis with Bayesian model
averaging (BMA), the methodology is robust to model mis-specification among
various possible relationships among the observable entities. Posterior
inference is demonstrated via application to a randomized controlled phase III
trial in metastatic colorectal cancer. Moreover, the article details posterior
predictive distributions of survival and statistical metrics for quantifying
the extent of direct and indirect, or tumor response mediated, treatment
effects.
