The Theory of Response‐Adaptive Randomization in Clinical Trials

Hu, Feifang; Rosenberger, William F.

doi:10.1002/047005588x

Cited by 335 publications

(282 citation statements)

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“…Fourth, other approaches to adjust the se-quential statistics could be developed. Finally, Hu and Rosenberger (2006) classified adaptive randomization procedures into four categories, i.e., restricted randomization, response-adaptive randomization (RAR), CAR and covariate-adjusted response-adaptive (CARA) randomization. Hu (2010, 2012) studied sequential monitoring of RAR in clinical trials.…”

Section: Resultsmentioning

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: Resultsmentioning

confidence: 99%

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials

Hu¹,

Hu²

2018

STAT SINICA

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“…In recent years, unequal allocation gains more applications in clinical trials [6], partially due to the emerging of Bayesian adaptive designs [7] and response adaptive randomization [8]. To evaluate the performance of different randomization designs under unequal allocations, the convergence guessing strategy has been extended from equal allocation to unequal allocation scenarios in recent publications [2][3][4][6][7][8][9][10][11][12]. In the book titled Selection bias and covariate imbalances in randomized clinical trials, Berger described two guessing strategies for unequal allocations under the names of convergent prediction and directional prediction with illustration examples [2].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Guessing strategies for treatment prediction under restricted randomization with unequal allocation

Zhao

Everett

Weng

et al. 2017

Contemporary Clinical Trials

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“…One of the reviewers pointed out another scenario that fits the paradigm of this paper: Response-adaptive randomization (see [15]) is based on two essential features, seeing ongoing results and changing probabilities accordingly which can change the allocation ratio substantially. Although response-adaptive randomization does this continually, the motivating example can be thought of as specific version of response-adaptive randomization where the adaptation does not occur until a substantial amount of data is available.…”

Section: Motivating Examplementioning

confidence: 99%

Maximum inflation of the type 1 error rate when sample size and allocation rate are adapted in a pre‐planned interim look

Gráf

Bauer

2011

Statistics in Medicine

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We calculate the maximum type 1 error rate of the pre-planned conventional fixed sample size test for comparing the means of independent normal distributions (with common known variance) which can be yielded when sample size and allocation rate to the treatment arms can be modified in an interim analysis. Thereby it is assumed that the experimenter fully exploits knowledge of the unblinded interim estimates of the treatment effects in order to maximize the conditional type 1 error rate. The "worst case" strategies require knowledge of the unknown common treatment effect under the null hypothesis. Although this is a rather hypothetical scenario it may be approached in practice when using a standard control treatment for which precise estimates are available from historical data. The maximum inflation of the type 1 error rate is substantially larger than derived by [1] for design modifications applying balanced samples before and after the interim analysis. Corresponding upper limits for the maximum type 1 error rate are calculated for a number of situations arising from practical considerations (e.g. restricting the maximum sample size, not allowing sample size decreases, allowing only increase of the sample size in the experimental treatment). The application is discussed for a motivating example.

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The Theory of Response‐Adaptive Randomization in Clinical Trials

Cited by 335 publications

References 0 publications

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials

Guessing strategies for treatment prediction under restricted randomization with unequal allocation

Maximum inflation of the type 1 error rate when sample size and allocation rate are adapted in a pre‐planned interim look

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