  Phase Ib/II oncology trials, despite their small sample sizes, aim to provide
information for optimal internal company decision-making concerning novel drug
development. Hybrid controls (a combination of the current control arm and
controls from one or more sources of historical trial data [HTD]) can be used
to increase the statistical precision. Here we assess combining two sources of
Roche HTD to construct a hybrid control in targeted therapy for decision-making
via an extensive simulation study. Our simulations are based on the real data
of one of the experimental arms and the control arm of the MORPHEUS-UC Phase
Ib/II study and two Roche HTD for atezolizumab monotherapy. We consider
potential complications such as model misspecification, unmeasured confounding,
different sample sizes of current treatment groups, and heterogeneity among the
three trials. We evaluate two frequentist methods (with both Cox and Weibull
accelerated failure time [AFT] models) and three different priors in Bayesian
dynamic borrowing (with a Weibull AFT model), and modifications within each of
those, when estimating the effect of treatment on survival outcomes and
measures of effect such as marginal hazard ratios. We assess the performance of
these methods in different settings and potential of generalizations to
supplement decisions in early-phase oncology trials. The results show that the
proposed joint frequentist methods and noninformative priors within Bayesian
dynamic borrowing with no adjustment on covariates are preferred, especially
when treatment effects across the three trials are heterogeneous. For
generalization of hybrid control methods in such settings we recommend more
simulation studies.
