Likelihood-Ratio E-Value Monitoring for Benchmark-Based Decisions in Early-Phase Oncology Trials

Abstract

Early-phase oncology trials often require protocol-ready interim rules for deciding whether an experimental regimen shows sufficient activity relative to prespecified clinical benchmarks. Existing Bayesian optimal phase II designs provide calibrated count boundaries for such decisions, but evidence is quantified through posterior probabilities and sample-size-dependent cutoff functions. We propose calibrated e-value monitoring (CEVAM), a likelihood-ratio evidence framework that defines monitoring evidence directly relative to prespecified clinical benchmarks, without requiring a Bayesian analysis prior or sample-size-dependent posterior-probability cutoff functions. For a binary efficacy endpoint, CEVAM constructs efficacy and reverse-futility e-processes targeted to clinically meaningful benchmark rates and converts them into monotone response-count boundaries. We distinguish a fixed-threshold e-process version, which has an anytime-valid interpretation under optional monitoring, from planned-look calibrated versions designed for conventional finite-look phase II trials. In simulations based on binary benchmark settings used in existing posterior-cutoff phase II designs, the proposed tuned planned-look rule controlled the nominal type I error and achieved the smallest expected sample size across all evaluated settings while retaining similar rejection probabilities. In an application to an actual phase II breast cancer trial, CEVAM classified the reported pathological complete response result as sufficient evidence for early success stopping. Extensions to categorical and multicomponent endpoints are provided in the Supplementary Material. CEVAM offers an analysis-prior-free, protocol-ready likelihood-ratio evidence scale for binary benchmark monitoring in early-phase oncology trials.