Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials

Ma, Wei; Hu, Feifang; Zhang, Li Xin

doi:10.1080/01621459.2014.922469

Cited by 56 publications

(89 citation statements)

References 41 publications

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“…A detailed discussion of CAR procedures can be found in Rosenberger and Sverdlov (2008). The theoretical properties of hypothesis testing based on CAR procedures have recently been developed by Shao et al (2010) and Ma et al (2015). However, both papers focused on the final test statistic instead of the sequential statistics (a stochastic process).…”

Section: Introductionmentioning

confidence: 99%

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials

Hu¹,

Hu²

2018

STAT SINICA

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The sequential monitoring of covariate-adaptive randomized clinical trials is standard in modern clinical studies. However, the validity of this sequential procedure is not well studied in the literature. Clinical trialists therefore implement the procedure and perform data analysis based on the theory of the sequential monitoring of fixed designs, and many clinical trials are open to question. In this paper, we study the theoretical properties of the sequential procedure and propose some important adjustments to classical statistical inference. Under different scenarios, we derive the asymptotic joint distribution of the sequential test statistics. Further, we estimate the decreased variability of the estimated treatment effect due to covariate-adaptive randomization, so that the sequential test statistics can be adjusted to be an asymptotic Brownian motion and the type I error rate can be controlled in real trials. Numerical results from simulation and the redesign of a clinical trial support our theoretical findings, showing that our procedure can control the type I error rate well, and also demonstrating the advantages of our method in terms of power and early stopping. Both theoretical and numerical results provide important guidance for future practical clinical trials using covariate-adaptive randomization procedures.

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Section: Introductionmentioning

confidence: 99%

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials

Hu¹,

Hu²

2018

STAT SINICA

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“…Recently, Ma and Hu consider this problem under a general framework. They derive the asymptotic distributions of the test statistics for testing both treatment effects and the significance of covariates under the null and alternative hypotheses.…”

Section: Inference Following Covariate‐adaptive Randomizationmentioning

confidence: 99%

Adaptive randomization for balancing over covariates

et al. 2014

WIREs Computational Stats

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In controlled clinical trials, balanced allocation over covariates is often viewed as an essential component in ensuring valid treatment comparisons. Minimization, sometimes called 'dynamic allocation', or 'covariate-adaptive randomization' has an advantage over stratified randomization, in that it is able to achieve balance over a large number of covariates when the sample size is small to medium. Despite its effectiveness, minimization has been questioned by regulatory agencies, mainly because of its increased complexity in practice and its potential impact on subsequent analysis. In recent years, however, with developments in clinical trials information technology, as well as advances in statistical theory, the attitudes toward minimization have evolved. In its 2013 draft guidelines, the European Medicines Agency (EMA) provided instructive guidelines for the implementation of minimization. In this paper we review the broad class of methods that belong to minimization, including its original forms for balancing over covariate margins and its generalization to balancing over other subgroups of interest or over continuous covariates. Moreover, we review the theoretical development in recent years, including the large-sample properties of balance under minimization, the impact of minimization on inference for different data types, and on suitable randomization tests.

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“…In terms of the bandwidth requirement, we find that as long as the bandwidth is chosen to be slightly larger than the covariate range, the results are not sensitive to the bandwidth choice. Not only does the asymptotic property of the covariate equilibrium D nu explain the covariate discrepancy between the arms, but it is also an essential component for analyzing the hypothesis testing procedures in the linear regression problem (Shao et al 2010, Ma et al 2015. Our theoretical results are essential for constructing inference procedures under the similarity weighted biased coin design.…”

Section: Real Data Examplementioning

confidence: 99%

Kernel-Based Adaptive Randomization Toward Balance in Continuous and Discrete Covariates

Jiang

Yin

2018

STAT SINICA

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Covariate balance among different treatment arms is critical in clinical trials, as confounding effects can be effectively eliminated when patients in different arms are alike. To balance the prognostic factors across different arms, we propose a new dynamic scheme for patient allocation. Our approach does not require discretizing continuous covariates to multiple categories, and can handle both continuous and discrete covariates naturally. This is achieved through devising a statistical measure to characterize the similarity between a new patient and all the existing patients in the trial. Under the similarity weighting scheme, we develop a covariate-adaptive biased coin design and establish its theoretical properties, as well as improving the original Pocock-Simon design. We conduct extensive simulation studies to examine the design operating characteristics and illustrate our method with a real data example. The new approach is demonstrated to be superior to other existing methods in terms of performance.

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Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials

Cited by 56 publications

References 41 publications

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials

Adaptive randomization for balancing over covariates

Kernel-Based Adaptive Randomization Toward Balance in Continuous and Discrete Covariates

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