Abstract

Seamless phase II/III trials have become a cornerstone of modern drug development, offering

a means to accelerate evaluation while maintaining statistical rigor.

However, most existing inference procedures are model-based, designed primarily for continuous outcomes, and often neglect the

stratification used in covariate-adaptive randomization (CAR), limiting their practical relevance. In

this paper, we propose a unified, model-robust framework for seamless phase II/III trials grounded

in generalized linear models (GLMs), enabling valid inference across diverse outcome types, estimands, and CAR schemes.

Using Z-estimation, we derive the asymptotic properties of treatment

effect estimators and explicitly characterize how their variance depends on the underlying randomization procedure. Based on these results, we develop adjusted Wald tests that, together with Dunnett’s

multiple-comparison procedure and the inverse–χ2 combination method, ensure valid overall Type I

error. Extensive simulation studies and a trial example demonstrate that the proposed model-robust

tests achieve superior power and reliable inference compared to conventional approaches.

Key words and phrases: Seamless phase II/III trial, covariate-adaptive randomization, generalized linear model, model-robust inference, bootstrap adjustment

Information

Preprint No.SS-2025-0427
Manuscript IDSS-2025-0427
Complete AuthorsKun Yi, Lucy Xia
Corresponding AuthorsLucy Xia
Emailslucyxia.2010@gmail.com

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Supplementary Materials

The Supplementary Material provides the proofs of the theoretical results and additional

numerical results.

Supplementary materials are available for download.