LAUSR

The use of backfill in early phase dose-finding trials is a relatively recent practice. It consists of assigning patients to dose levels below the level where the study is at. The main reason for backfilling is to collect additional pharmacokinetic, pharmacodynamic and response data, in order to assess whether a plateau may exist on the dose-efficacy curve. This is a possibility in oncology with molecularly targeted agents or immunotherapy. Recommending for further study a dose level lower than the maximum tolerated dose could be supported in such situations. How to best allocate backfill patients to dose levels is not yet established. In this paper we propose to randomise backfill patients below the dose level where the study is at. A refinement of this would be to stop backfilling to lower dose levels when these show insufficient efficacy compared to higher levels, starting at dose level 1 and repeating this process sequentially. At study completion, data from all patients (both backfill patients and dose-finding patients) is used to estimate the dose-response curve. The fit from a change point model is compared to the fit of a monotonic model to identify a potential plateau. Using simulations, we show that this approach can identify the plateau on the dose-response curve when such a plateau exists, allowing the recommendation of a dose level lower than the maximum tolerated dose for future studies. This contribution provides a methodological framework for backfilling, from the perspective of both design and analysis in early phase oncology trials.