A Bayesian Optimal Phase II Design for Randomized Immunotherapy Trials with Delayed Treatment Effects

Abstract

Immunotherapy has transformed cancer treatment, yet its delayed therapeutic effects often lead to non-proportional hazards, rendering many conventional phase II designs underpowered and prone to type I error inflation. To address this issue, we propose a novel Bayesian Optimal Phase II design (DTE-BOP2) that explicitly models the uncertainty in the separation timing of treatment effect. The treatment separation timepoint (denoted by S) is endowed with a truncated-Gamma prior, whose parameters can be elicited from experts or inferred from historical data, with default settings available when prior knowledge is scarce. Built upon the BOP2 framework (Zhou et al. 2017, 2020), our design retains operational simplicity while incorporating type I error control and maintaining the power. Extensive simulations demonstrate that DTE-BOP2 uniformly controls type I error at the nominal level across a wide range of treatment effect separation timepoint S. We further observe that the power decreases monotonically as S increases. Importantly, we find that the power is primarily driven by the relative magnitude of treatment benefit before and after the separation time, i.e., the ratio of medians, rather than their absolute values. Compared to the original BOP2, the piecewise weighted log-rank, and the conventional log-rank tests, DTE-BOP2 achieves higher power with smaller sample sizes while preserving type I error robustness across plausible delay scenarios. An open-source R package, DTEBOP2 (CRAN), with detailed vignettes, enables investigators to implement the design and analyse phase-II trials exhibiting delayed treatment effects.