Statistical Inference for Covariate-Adaptive Randomization Procedures

Ma, Wei; Qin, Yichen; Li, Yang; Hu, Feifang

doi:10.1080/01621459.2019.1635483

Cited by 43 publications

(43 citation statements)

References 27 publications

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“…The power degradation is attributed to the loss of information caused by the discretization of continuous covariates, which corresponds to the additional terms of

σ_{δ_{k}}^{2}, k \in C^{*},

in the definition of

τ^{2}

compared with the discrete case. Such a phenomenon is also seen in other studies; see, for example, table 4 of Ma et al 28 When the “Full data” case is used in analysis, the different designs show similar performance with respect to power. However, SPB, PS, and HH are more powerful than CR when some or none of the covariates are used in inference.…”

Section: Numerical Studiessupporting

confidence: 73%

“…In addition, our current work only considered models in which the responses are assumed to be linear, but there are also cases in which the responses cannot be modeled linearly. Recently, a few theoretical works have studied the incorporation of logistic regression in CAR design 28,38 and pointed out that the nonlinearity poses some difficulties in correcting type I error. Therefore, extending the current method to other nonlinear models may be a valuable topic for future research.…”

Section: Conclusion and Remarksmentioning

confidence: 99%

“…However, it is also important to consider continuous covariates in the design and to use them directly without discretization. No statistical inference for CAR with continuous covariates was available until recent years, 28,37 and it would be interesting to extend our work to incorporate continuous covariates in the future. It would be also interesting to extend the proposed work to designs with more DATA AVAILABILITY STATEMENT The numerical studies reported in Section 5 were based on the data from clinical trials conducted by the National Institute on Drug Abuse (NIDA).…”

Section: Designsmentioning

confidence: 99%

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Sample size re‐estimation for covariate‐adaptive randomized clinical trials

2021

Statistics in Medicine

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Covariate‐adaptive randomization (CAR) procedures have been developed in clinical trials to mitigate the imbalance of treatments among covariates. In recent years, an increasing number of trials have started to use CAR for the advantages in statistical efficiency and enhancing credibility. At the same time, sample size re‐estimation (SSR) has become a common technique in industry to reduce time and cost while maintaining a good probability of success. Despite the widespread popularity of combining CAR designs with SSR, few researchers have investigated this combination theoretically. More importantly, the existing statistical inference must be adjusted to protect the desired type I error rate when a model that omits some covariates is used. In this article, we give a framework for the application of SSR in CAR trials and study the underlying theoretical properties. We give the adjusted test statistic and derive the sample size calculation formula under the CAR setting. We can tackle the difficulties caused by the adaptive features in CAR and prove the asymptotic independence between stages. Numerical studies are conducted under multiple parameter settings and scenarios that are commonly encountered in practice. The results show that all advantages of CAR and SSR can be preserved and further improved in terms of power and sample size.

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“…The power degradation is attributed to the loss of information caused by the discretization of continuous covariates, which corresponds to the additional terms of

σ_{δ_{k}}^{2}, k \in C^{*},

in the definition of

τ^{2}

Section: Numerical Studiessupporting

confidence: 73%

Section: Conclusion and Remarksmentioning

confidence: 99%

Section: Designsmentioning

confidence: 99%

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Sample size re‐estimation for covariate‐adaptive randomized clinical trials

2021

Statistics in Medicine

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“…In particular, it is desirable to extend the current framework to the CAR methods that balance covariates within their margins, such as Pocock and Simon's minimization. It is also of interest to consider the methods balancing continuous covariates (Ma et al ., 2019). Second, this paper focuses on testing the treatment effect.…”

Section: Discussionmentioning

confidence: 99%

“…The randomization test is almost assumption‐free and can generally preserve the Type I error, although it is computationally intensive and may cause a loss of power in some cases. In the literature, some studies have compared the randomization and model‐based tests under CAR (Hasegawa and Tango, 2009; Ma et al ., 2019), but covariate misclassification was not taken into account. We next briefly explore the utility of the randomization test and compare it with model‐based tests in the context of CAR with covariate misclassification.…”

Section: Clinical Examplementioning

confidence: 99%

The impact of misclassification on covariate‐adaptive randomized clinical trials

Wang

2020

Biometrics

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Covariate‐adaptive randomization (CAR) is widely used in clinical trials to balance treatment allocation over covariates. Over the past decade, significant progress has been made on the theoretical properties of covariate‐adaptive design and associated inference. However, most results are established under the assumption that the covariates are correctly measured. In practice, measurement error is inevitable, resulting in misclassification for discrete covariates. When covariate misclassification is present in a clinical trial conducted using CAR, the impact is twofold: it impairs the intended covariate balance, and raises concerns over the validity of test procedures. In this paper, we consider the impact of misclassification on covariate‐adaptive randomized trials from the perspectives of both design and inference. We derive the asymptotic normality, and thereby the convergence rate, of the imbalance of the true covariates for a general family of covariate‐adaptive randomization methods, and show that a superior covariate balance can still be attained compared to complete randomization. We also show that the two sample t‐test is conservative, with a reduced Type I error, but that this can be corrected using a bootstrap method. Moreover, if the misclassified covariates are adjusted in the model used for analysis, the test maintains its nominal Type I error, with an increased power. Our results support the use of covariate‐adaptive randomization in clinical trials, even when the covariates are subject to misclassification.

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Incorporating covariates information in adaptive clinical trials for precision medicine

Zhao

Wang

et al. 2021

Pharmaceutical Statistics

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Precision medicine is the systematic use of information that pertains to an individual patient to select or optimize that patient's preventative and therapeutic care. Recent studies have classified biomarkers into predictive and prognostic biomarkers based on their roles in clinical studies. To design a clinical trial for precision medicine, predictive biomarkers and prognostic biomarkers should both be included. In statistical analysis, biomarkers are mathematically treated as covariates. We first classify covariates into predictive and prognostic covariates according to their roles. We then provide a brief review of recent advances in adaptive designs that incorporate covariates. However, the literature includes no designs that incorporate both prognostic covariates and predictive covariates simultaneously. In this paper, we propose a new family of covariate-adjusted response-adaptive (CARA) designs that incorporate both prognostic and predictive covariates and the responses. It is important to note that the predictive biomarkers and prognostic biomarkers play different roles in the new designs. The advantages of the proposed methods are demonstrated via numerical studies, and some further statistical issues are also discussed.

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Statistical Inference for Covariate-Adaptive Randomization Procedures

Cited by 43 publications

References 27 publications

Sample size re‐estimation for covariate‐adaptive randomized clinical trials

Sample size re‐estimation for covariate‐adaptive randomized clinical trials

The impact of misclassification on covariate‐adaptive randomized clinical trials

Incorporating covariates information in adaptive clinical trials for precision medicine

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