Dose-finding trials are a key component of the drug development process and
rely on a statistical design to help inform dosing decisions. Triallists
wishing to choose a design require knowledge of operating characteristics of
competing methods. This is often assessed using a large-scale simulation study
with multiple designs and configurations investigated, which can be
time-consuming and therefore limits the scope of the simulation.
We introduce a new approach to the design of simulation studies of
dose-finding trials. The approach simulates all potential outcomes that
individuals could experience at each dose level in the trial. Datasets are
simulated in advance and then the same datasets are applied to each of the
competing methods to enable a more efficient head-to-head comparison.
In two case-studies we show sizeable reductions in Monte Carlo error for
comparing a performance metric between two competing designs. Efficiency gains
depend on the similarity of the designs. Comparing two Phase I/II design
variants, with high correlation of recommending the same optimal biologic dose,
we show that the new approach requires a simulation study that is approximately
30 times smaller than the conventional approach. Furthermore, advance-simulated
trial datasets can be reused to assess the performance of designs across
multiple configurations.
We recommend researchers consider this more efficient simulation approach in
their dose-finding studies and we have updated the R package escalation to help
facilitate implementation.Comment: 27 pages, 4 figures, 1 tabl