  The design and conduct of platform trials have become increasingly popular
for drug development programs, attracting interest from statisticians,
clinicians and regulatory agencies. Many statistical questions related to
designing platform trials - such as the impact of decision rules, sharing of
information across cohorts, and allocation ratios on operating characteristics
and error rates - remain unanswered. In many platform trials, the definition of
error rates is not straightforward as classical error rate concepts are not
applicable. In particular, the strict control of the family-wise Type I error
rate often seems unreasonably rigid. For an open-entry, exploratory platform
trial design comparing combination therapies to the respective monotherapies
and standard-of-care, we define a set of error rates and operating
characteristics and then use these to compare a set of design parameters under
a range of simulation assumptions. When setting up the simulations, we aimed
for realistic trial trajectories, e.g. in case one compound is found to be
superior to standard-of-care, it could become the new standard-of-care in
future cohorts. Our results indicate that the method of data sharing, exact
specification of decision rules and quality of the biomarker used to make
interim decisions all strongly contribute to the operating characteristics of
the platform trial. Together with the potential flexibility and complexity of a
platform trial, which also impact the achieved operating characteristics, this
implies that utmost care needs to be given to evaluation of different
assumptions and design parameters at the design stage.
