Statistical inference of adaptive randomized clinical trials for personalized medicine

Hu, Feifang; Hu, Yanqing; Ma, Wei; Zhang, Lixin; Zhu, Hongjian

doi:10.4155/cli.15.15

Cited by 9 publications

(7 citation statements)

References 63 publications

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“…In particular, with the development of bioinformatics, the association between biomarkers and disease has become widely accepted. In the era of personalized medicine, it is desirable to incorporate covariates into clinical trial designs that investigate the heterogeneity of patients' responses to a treatment (Hu, 2012;Hu et al, 2015). The study results may be invalid if there is treatment imbalance over the covariates.…”

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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“…For reviews of the latter three types of randomization, see [12], [13], and [24]. Within this framework it is clear that our generalized Efron's biased coin design belongs to restricted randomization.…”

Section: General Definitionmentioning

confidence: 99%

“…P (d, d (k) ) = P( N n+1 = e k | D n = d) = p(k, d). (13) Substituting (11), (12), and (13) into (10), we have…”

mentioning

confidence: 99%

Generalized Efron's biased coin design and its theoretical properties

2016

J. Appl. Probab.

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In clinical trials with two treatment arms, Efron's biased coin design, Efron (1971), sequentially assigns a patient to the underrepresented arm with probability p > ½. Under this design the proportion of patients in any arm converges to ½, and the convergence rate is n-1, as opposed to n-½ under some other popular designs. The generalization of Efron's design to K ≥ 2 arms and an unequal target allocation ratio (q1, . . ., qK) can be found in some papers, most of which determine the allocation probabilities ps in a heuristic way. Nonetheless, it has been noted that by using inappropriate ps, the proportion of patients in the K arms never converges to the target ratio. We develop a general theory to answer the question of what allocation probabilities ensure that the realized proportions under a generalized design still converge to the target ratio (q1, . . ., qK) with rate n-1.

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“…[59] explored the derivation of optimal allocation proportion. Other discussions of the advantages of RAR can be found in [2,8,21,26,27,30,41]. Clinical trials using RAR designs include [1,42,53].…”

Section: Introductionmentioning

confidence: 99%

Seamless Clinical Trials with Doubly Adaptive Biased Coin Designs

Zhu¹,

Yu²,

Lai³

et al. 2023

The New England Journal of Statistics in Data Science

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In addition to scientific questions, clinical trialists often explore or require other design features, such as increasing the power while controlling the type I error rate, minimizing unnecessary exposure to inferior treatments, and comparing multiple treatments in one clinical trial. We propose implementing adaptive seamless design (ASD) with response adaptive randomization (RAR) to satisfy various clinical trials’ design objectives. However, the combination of ASD and RAR poses a challenge in controlling the type I error rate. In this paper, we investigated how to utilize the advantages of the two adaptive methods and control the type I error rate. We offered the theoretical foundation for this procedure. Numerical studies demonstrated that our methods could achieve efficient and ethical objectives while controlling the type I error rate.

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Statistical inference of adaptive randomized clinical trials for personalized medicine

Cited by 9 publications

References 63 publications

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials

Generalized Efron's biased coin design and its theoretical properties

Seamless Clinical Trials with Doubly Adaptive Biased Coin Designs

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