Gernot Wassmer and Werner Brannath. GROUP SEQUENTIAL AND CONFIRMATORY ADAPTIVE DESIGNS IN CLINICAL TRIALS.(2016). Heidelberg: Springer. 301 pages, ISBN: 9783319325606

Covariate-adaptive designs are widely used to balance covariates and maintain randomization in clinical trials. Adaptive designs for discrete covariates and their asymptotic properties have been well studied in the literature. However, important continuous covariates are often involved in clinical studies. Simply discretizing or categorizing continuous covariates can result in loss of information. The current understanding of adaptive designs with continuous covariates lacks a theoretical foundation as the existing works are entirely based on simulations. Consequently, conventional hypothesis testing in clinical trials using continuous covariates is still not well understood. In this paper, we establish a theoretical framework for hypothesis testing on adaptive designs with continuous covariates based on linear models. For testing treatment effects and significance of covariates, we obtain the asymptotic distributions of the test statistic under null and alternative hypotheses. Simulation studies are conducted under a class of covariate-adaptive designs, including the p-value-based method, the Su’s percentile method, the empirical cumulative-distribution method, the Kullback–Leibler divergence method, and the kernel-density method. Key findings about adaptive designs with independent covariates based on linear models are (1) hypothesis testing that compares treatment effects are conservative in terms of smaller type I error, (2) hypothesis testing using adaptive designs outperforms complete randomization method in terms of power, and (3) testing on significance of covariates is still valid.

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Designing one-sided group sequential clinical trials to detect a mixture alternative

Sequential Analysis ◽

10.1080/07474946.2018.1466545 ◽

2018 ◽

Vol 37 (2) ◽

pp. 268-291

Author(s):

Hua Peng ◽

Daniel R. Jeske ◽

Ashis SenGupta ◽

Weixin Yao

Keyword(s):

Clinical Trials ◽

Group Sequential

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Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

10.21203/rs.3.rs-943138/v1 ◽

2021 ◽

Author(s):

Elja Arjas ◽

Dario Gasbarra

Keyword(s):

Clinical Trials ◽

Randomized Clinical Trials ◽

Statistical Significance ◽

Adaptive Designs ◽

Critical Threshold ◽

Attractive Alternative ◽

Posterior Probabilities ◽

Statistical Significance Testing ◽

Treatment Allocation ◽

Frequentist Paradigm

Abstract Background: Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power. Results: The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during Phase II and III. This approach is based on comparing the performance of the different treatment arm in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm (Rule 1), and treatment selection, removing an arm from the trial permanently (Rule 2). The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package. Conclusion: The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

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A New Procedure for Group Sequential Analysis in Clinical Trials

Biometrics ◽

10.2307/2532297 ◽

1992 ◽

Vol 48 (2) ◽

pp. 373 ◽

Cited By ~ 12

Author(s):

Bruno Falissard ◽

Joseph Lellouch

Keyword(s):

Clinical Trials ◽

Sequential Analysis ◽

Group Sequential ◽

Group Sequential Analysis

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