The efficient randomized-adaptive design attains the minimal possible variance of the allocation proportion for any pre-specified target and has been extended from two to several treatment arms, but both versions target a single response. Confirmatory trials increasingly rely on several prioritized endpoints, typically efficacy followed by safety, that cannot be reduced to one summary without losing information. The efficient randomized-adaptive design is extended here to targets driven by the net treatment benefit of generalized pairwise comparisons, allowing any number of arms and endpoints of mixed type, including censored time-to-event outcomes. Strong consistency, a law of the iterated logarithm, and asymptotic normality of the allocation proportions are established by combining a sequential H\'ajek projection for the net benefit with the martingale technique used for the original design, and the design is shown to attain the semiparametric efficiency bound within the class of designs that track a smooth function of the net benefit. In simulations calibrated to a three-arm phase III melanoma trial, the proposed design concentrates allocation on the superior treatment more efficiently than designs driven by a single endpoint, while preserving type I error and power. A redesign of the trial illustrates the practical benefit, and the approach is discussed in relation to current regulatory thinking on adaptive designs for confirmatory trials.
A new inference method for conducting multiple-treatment comparisons involving endpoints within the generalized linear model (GLM) framework under covariate-adaptive randomization (CAR) that can effectively control Type I error while potentially improving power.
This work proposes a novel data-driven, adaptive procedure that identifies the optimal restriction time from a continuous range by maximizing a criterion balancing effect size and estimation precision, and provides a rigorous theoretical foundation that accounts for the additional variability introduced by adaptive sel...
Jing-Hao Sun, D. Schaubel, E. T. Tchetgen Tchetgen· Biometrika· 0 citations
The log-rank test and Kaplan--Meier plot are standard tools for analyzing time-to-event data in randomized clinical trials, yet neither provides a summary of the magnitude of the treatment effect. Practitioners typically fill this gap by reporting a hazard ratio from a Cox proportional-hazards model or an acceleration...
Lucy D'Agostino McGowan, Joseph Rigdon, Xin-Ran Li et al.· 0 citations
A flexible phase II design framework for two primary endpoints with nested response metrics is developed and the Holm-type and Holm-type designs can reduce the required sample size while maintaining prespecified error-rate and power requirements.
Jiangtao Gou, F. Zhang· Contemporary Clinical Trials· 0 citations
We introduce a flexible adaptive design strategy for treatment allocation in clinical trials based on a randomized simulated annealing algorithm. The proposed approach provides a unified and modular framework for implementing a broad range of adaptive randomization objectives, including covariate-adaptive (CA), respons...
Rosamarie Frieri, Francesco Mariani, M. Novelli· Statistical Methods in Medic...· 0 citations
I study the optimal design and analysis of randomized experiments for estimating finite-population average treatment effects when potential outcomes are known to be bounded, as with binary outcomes. Among all assignment mechanisms and a broad class of affine estimators, worst-case mean-squared error (MSE) is minimized...
Peter Hull· 0 citations
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