  When planning an oncology clinical trial, the usual approach is to assume
proportional hazards and even an exponential distribution for time-to-event
endpoints. Often, besides the gold-standard endpoint overall survival (OS),
progression-free survival (PFS) is considered as a second confirmatory
endpoint. We use a survival multistate model to jointly model these two
endpoints and find that neither exponential distribution nor proportional
hazards will typically hold for both endpoints simultaneously. The multistate
model provides a stochastic process approach to model the dependency of such
endpoints neither requiring latent failure times nor explicit dependency
modelling such as copulae. We use the multistate model framework to simulate
clinical trials with endpoints OS and PFS and show how design planning
questions can be answered using this approach. In particular, non-proportional
hazards for at least one of the endpoints are naturally modelled as well as
their dependency to improve planning. We consider an oncology trial on
non-small-cell lung cancer as a motivating example from which we derive
relevant trial design questions. We then illustrate how clinical trial design
can be based on simulations from a multistate model. Key applications are
co-primary endpoints and group-sequential designs. Simulations for these
applications show that the standard simplifying approach may very well lead to
underpowered or overpowered clinical trials. Our approach is quite general and
can be extended to more complex trial designs, further endpoints, and other
therapeutic areas. An R package is available on CRAN.
