Oncology clinical trial design planning based on a multistate model that jointly models progression-free and overall survival endpoints
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When planning an oncology clinical trial, the usual approach is to assume an exponential distribution for the time-to-event endpoints. Often, besides the gold-standard endpoint overall survival, progression-free survival 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 approach allows us to consider the joint distribution of the two endpoints and to derive quantities of interest as the correlation between overall survival and progression-free survival. In this paper, 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 addition to the major advantage that we can model non-proportional hazards quite naturally with this approach, the correlation between the two endpoints can be exploited to determine sample size and type-I-error. 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 often leads 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.
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