Statistical considerations of designs for single-arm proof-of-concept oncology trial with time-to-event endpoint

Single-arm proof-of-concept (PoC) clinical studies are widely used to accelerate the signal-finding process in oncology drug development before or in lieu of randomized PoC studies. Traditionally the primary endpoint for single-arm PoC studies is objective response rate (ORR). However, in cases that ORR is not applicable or not clinically relevant, time-to-event (TTE) endpoint is used instead. One conventional approach is to dichotomize the TTE endpoint as a binary endpoint to assess the survival rate, which may compromise the testing efficiency due to the requirement of minimum follow-up without censoring. Alternatively, we can use the non-parametric one-sample log-rank test (OSLRT) to evaluate the survival curve difference compared with historical control. This approach can incorporate censoring and all time-point information on the survival curve, but the test statistic may be difficult to interpret and quantify the magnitude of treatment effect. Given that clinicians are more interested in the survival rate at a clinically relevant landmark timepoint, we can also use a landmark Kaplan-Meier method (LMKM) to estimate the survival rate at a landmark timepoint for design and analysis of single-arm proof-of-concept oncology trials with TTE endpoint. This non-parametric method is straightforward to clinicians and can be applied to any survival models. Simulation studies show that the LMKM method can improve the efficiency upon the binary survival rate approach and achieve comparable operating characteristics as the one-sample log-rank test. We also develop an R package for the implementation of these mainstream designs, which fills the gap of available software for design and analysis of single-arm studies with TTE endpoint.